Cardiac mapping with catheter shape information

ABSTRACT

A non-contact cardiac mapping method is disclosed that includes: (i) inserting a catheter into a heart cavity having an endocardium surface, the catheter including multiple, spatially distributed electrodes; (ii) measuring signals at the catheter electrodes in response to electrical activity in the heart cavity with the catheter spaced from the endocardium surface; and (iii) determining physiological information at multiple locations of the endocardium surface based on the measured signals and positions of the electrodes with respect to the endocardium surface. Related systems and computer programs are also disclosed.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.13/044,982, filed Mar. 10, 2011, which is a continuation of U.S.application Ser. No. 12/791,456, filed Jun. 1, 2010, now U.S. Pat. No.7,957,792, which is a continuation of U.S. application Ser. No.11/451,871, filed Jun. 13, 2006, now U.S. Pat. No. 7,729,752, all ofwhich are herein incorporated by reference in their entirety.

TECHNICAL FIELD

This invention relates to the determination and representation ofphysiological information relating to a heart surface using anon-contact catheter.

BACKGROUND

Cardiac arrhythmias are a leading cause of stroke, heart disease andsudden death. The physiological mechanism of arrhythmia involves anabnormality in the electrical conduction of the heart. There are anumber of treatment options for patients with arrhythmia which includemedication, implantable devices, and minimally invasive procedures.

Minimally invasive procedures, such as catheter ablation, have evolvedin recent years to become an established treatment for patients with avariety of supraventricular and ventricular arrhythmias. A typicalminimally invasive procedure involves mapping of the heart tissue inorder to identify the site of origin of the arrhythmia followed by atargeted ablation of the site. Other minimally invasive proceduresinvolve the delivery of biological agents such as cells or genes as aform of therapy to the identified site of origin of the arrhythmia. Theprocedure takes place in an electrophysiology laboratory and takesseveral hours, most of which is spent mapping the electrical conductionin the heart.

Conventional 3D mapping techniques include contact mapping andnon-contact mapping. In contact mapping techniques one or more cathetersare advanced into the heart. Physiological signals resulting from theelectrical activity of the heart are acquired with one or moreelectrodes located at the catheter distal tip after determining that thetip is in stable and steady contact with the endocardium surface of aparticular heart chamber. Location and electrical activity is usuallymeasured sequentially on a point-by-point basis at about 50 to 200points on the internal surface of the heart to construct anelectro-anatomical depiction of the heart. The generated map may thenserve as the basis for deciding on a therapeutic course of action, forexample, tissue ablation, to alter the propagation of the heart'selectrical activity and to restore normal heart rhythm. Although theelectrode(s) contacting the endocardium surface enable a relativelyfaithful acquisition of physiological signals with minimal signaldegradation, contact-based mapping techniques tend to be time consumingsince the catheter, and thus its electrodes, have to be moved to arelatively large number of locations in the heart cavity to acquiresufficient data to construct the electro-anatomical depiction of theheart. Additionally, moving the catheter to different locations so thatthe catheter's electrode(s) touch the endocardium is a cumbersomeprocess that is technically challenging. Further complicating thecontact-based mapping methodology is the occurrence of unstablearrhythmias condition. Particularly, ventricular tachyarrhythmias maycompromise the heart's ability to circulate blood effectively. As aresult, the patient cannot be maintained in fast tachyarrhythmia's formore than a few minutes, which significantly complicates the ability tomap during the arrhythmia. In addition, some arrhythmia's are transientor non-periodic in nature. Contact-based sequential mapping, therefore,is less suitable for mapping these arrhythmia's since the sequentialcontact-based methodology is predicated on the assumption that recordedsignals are periodic in nature.

On the other hand, in non-contact-based mapping systems a multipleelectrodes catheter is percutaneously placed in the heart chamber ofinterest. Once in the chamber, the catheter is deployed to assume a 3Dshape. Using the signals detected by the non-contact electrodes andinformation on chamber anatomy and relative electrode location, thesystem provides physiological information regarding the endocardium ofthe heart chamber. Although non-contact mapping techniques cansimultaneously acquire signals using the multiple electrodes catheterand thus enable faster reconstruction of the electrical activity on theendocardial surface, because the catheter's multiple electrodes are notin contact with the endocardium surface some loss of accuracy of thereconstructed map, which is proportional to the distance from theendocardium, occurs due to the degradation of the signals acquired bythe multiple electrodes. Moreover, the computation of the complextransformations required to transform the signals acquired by thecatheter's electrodes to determine the corresponding reconstructedinformation at the endocardium surface is relatively time consuming.Also, the accuracy of the reconstructed information is constrained bythe number of electrodes that can be attached to the catheter.

SUMMARY

In one aspect, a non-contact cardiac mapping method is disclosed thatincludes: (i) inserting a catheter into a heart cavity having anendocardium surface, the catheter including multiple, spatiallydistributed electrodes; (ii) measuring signals at the catheterelectrodes in response to electrical activity in the heart cavity withthe catheter spaced from the endocardium surface; and (iii) determiningphysiological information at multiple locations of the endocardiumsurface based on the measured signals and positions of the electrodeswith respect to the endocardium surface.

Embodiments may further include one or more of the following features:

The method may further include moving the catheter to each of multiple,different positions in the heart cavity for which the catheter is spacedfrom the endocardium surface, and, for each of the different catheterpositions, determining the positions of the catheter electrodes withrespect to the endocardium surface and measuring signals at the catheterelectrodes in response to electrical activity in the heart cavity. Thedetermination of the physiological information at the multiple locationsof the endocardium surface is based further on the positions of thecatheter electrodes and the measured signals at the different catheterpositions.

The number of catheter positions at which the signals are measured andused to determine the physiological information at the multiplelocations of the endocardium surface may be more than three. In someembodiments the number of catheter positions at which the signals aremeasured is more than five, and in some other embodiments the number ofcatheter positions at which the signals are measured is more than ten.

Typically, the catheter is moved over a range larger than about onethird of the diameter of the heart cavity to measure the signals used todetermine the physiological information at the multiple locations of theendocardium surface.

The signals may be measured for at least one electrical heart cycle foreach catheter position.

The determination of the physiological information at the multiplelocations of the endocardium surface may include synchronizing thesignals measured at the different catheter positions with one anotheraccording to an electrical heart beat cycle.

The measured signals may be synchronized based on physiological dataincluding, for example, ECG and/or intercardiac electrograms.

The determination of the physiological information at the multiplelocations of the endocardium surface may further include processing thesynchronized signals as though they were obtained at one time from allof the positions sampled by the catheter electrodes for the differentpositions of the catheter in the heart cavity.

The determination of the physiological information at the multiplelocations of the endocardium surface may further include applying atransformation function to the synchronized signals. The transformationfunction relates signals measured from at least some of the differentpositions of the catheter in the heart cavity to the physiologicalinformation at the multiple locations of the endocardium surface.

The determination of the physiological information at the multiplelocations of the endocardium surface may further include determining thetransformation function by calculating a forward transformation forrelating the physiological information at the multiple locations of theendocardium surface to the signals measured for the different positionsof the catheter in the heart cavity and inverting the forwardtransformation. Inverting the forward transformation may includereformulating an underdetermined matrix inversion by regularization.Inverting may include a least squares minimization.

The determination of the physiological information at the multiplelocations of the endocardium surface may include determining multipleestimates of the physiological information for each of at least some ofthe locations on the endocardium based on the measured signalscorresponding to at least some of the different catheter positions. Themethod may include processing the multiple estimates to improve anaccuracy of the physiological information. The processing of themultiple estimates may also include averaging the estimates. Averagingmay be a weighted averaging.

Determining the position of the catheter electrodes with respect to theendocardium surface may include using, for example, electric fields,magnetic fields, fluoroscopy, and/or ultrasound to determine a positionof the catheter in a first coordinate system. Determining the positionof the catheter with respect to the endocardium surface may furtherinclude registering a representation of the endocardium surface with thefirst coordinate system, the representation having been obtained priorto inserting the catheter into the heart cavity.

The signals may be measured during multiple electrical heart beatcycles, and the physiological information may be determined, at least inpart, by combining information derived from the signals for differentheart beat cycles.

The combining may include integrating information derived from thesignals for common phases of the electrical heart beat cycles. Theintegrated information may include integrated electric potentials on theendocardium surface for common phases of the multiple electrical heartcycle.

The information derived from the signals for different heart beat cyclesmay include, for example, a maximum voltage amplitude for each of thedifferent heart beat cycles at different ones of the endocardium surfacelocations. The combining may include averaging together the maximumvoltage amplitudes for the different heart beat cycles. The averagingmay be a weighted averaging.

The method may further include generating a representation of theendocardium surface of a patient's heart cavity prior to inserting thecatheter into the heart cavity, and registering the representation ofthe endocardium surface with a first coordinate system used to determinethe positions of the catheter electrodes relative to the endocardiumsurface after the catheter is inserted into the heart cavity.

The determination of the physiological information at the multiplelocations of the endocardium surface may be based on the positions ofthe catheter electrodes, the measured signals at the different catheterpositions and the registered representation of the endocardium surface.

Generating the representation of the endocardium surface may includesegmenting a volumetric representation of the heart cavity. Thevolumetric representation may be obtained from, for example, a coherencetomography (CT) image, a magnetic resonance imaging (MRI) image and/oran ultrasound image. The volumetric representation may be segmented intoa substantially closed surface representation of the endocardiumsurface.

Generating the representation of the endocardium surface may includepartitioning the representation into a plurality of surface elementsbased on, for example, a numerical calculation technique applied tofacilitate computing of the physiological information at the endocardiumsurface and/or characteristics of the endocardium surface. Therepresentation partitioned into a plurality of elements may include, forexample, a surface mesh comprising triangles, a volumetric meshcomprising tetrahedra and/or a regular Cartesian grid.

The method may further include contacting the catheter to theendocardium surface at multiple locations to establish multiple pointsof the endocardium surface in the first coordinate system. The methodmay also include determining the position of the catheter when itcontacts the endocardium surface at the multiple locations using, forexample, electric fields, magnetic fields, fluoroscopy, and/orultrasound to determine the position of the catheter in the firstcoordinate system. Registering may include translating and orienting thesurface representation in the first coordinate system to fit theestablished points of the endocardium surface in the first coordinatesystem.

The determining of the physiological information may include, prior toinserting the catheter into the heart cavity, processing informationrelating to characteristics of the endocardium surface.

Processing information relating to characteristics of the endocardiumsurface may include partially computing one or more transformationfunctions for converting the signals measured at the catheter electrodesto estimates of the physiological information at the endocardiumsurface.

In some embodiments, each transformation function may be associated witha different position within the heart cavity.

In some embodiments, each transformation function may be associated witha different position and orientation of the catheter within the heartcavity.

In some embodiments, each transformation function may be associated withthe respective positions of the catheter electrodes within the heartcavity.

Determining of the physiological information may further include, priorto inserting the catheter into the heart cavity, processing informationrelating to the characteristics of the catheter.

The characteristics of the endocardium surface may be derived from apre-acquired representation of the endocardium surface.

The processing prior to inserting the catheter into the heart cavity maybe performed to expedite the determination of the physiologicalinformation at the multiple locations of the endocardium surface fromthe measured signals.

The transformation functions may be inverse transformation functions,where the partial computation of the one or more inverse transformationfunctions may include at least partially computing one or more forwardtransformation functions for determining the signals measured at thecatheter electrodes from the physiological information at the multiplelocations of the endocardium surface, each forward transformationfunction being associated with the position of one or more catheterelectrodes within the heart cavity. Each forward transformation functionmay further be associated with an orientation of the catheter within theheart cavity. The catheter may be hollow.

At least partially computing the one or more forward transformations mayinclude processing information relating to the shape of the endocardiumsurface.

At least partially computing the one or more forward transformations mayinclude processing information relating to the distribution of theelectrodes on the catheter.

At least partially computing the one or more forward transformations mayinclude fully computing the one or more forward transformations based onthe information relating to at least the shape of the endocardiumsurface.

Determining the physiological information from the measured signals mayinclude inverting the forward transformation function associated withthe position of the catheter used to measure the signals and applyingthe inverted forward transformation function to the measured signals.Inverting the forward transformation may include reformulating anunderdetermined matrix inversion by regularization. Inverting mayfurther include performing a least squares minimization. The inversionmay further include a Tikhonov regularization.

The one or more transformation functions can be expressed as one or morematrices.

The method may further include computing values indicative of a degreeof spatial resolution of the determined physiological information for atleast some of the locations of the endocardium surface.

The computed values may be derived, at least in part, from atransformation function relating the physiological information at themultiple locations of the endocardium surface to the signals measured bythe catheter electrodes.

The method may further include displaying at least a portion of theendocardium surface to include at least some of the computed resolutionvalues. The method may further include overlaying on a display device atleast some of the physiological information determined at the multiplesurface locations.

The method may further include displaying at least a portion of theendocardium surface to include a selected subset of the physiologicalinformation determined at the multiple surface locations, where thesubset is selected based on at least some of the computed resolutionvalues.

The determined physiological information may include electricalpotential values at the multiple locations of endocardium surface atdifferent phases of the heart beat cycle. The method further may includedetermining frequency dependent features of converting the electricalpotential values into a frequency representation of electrical activityat multiple locations of the endocardium surface during the heart beatcycle.

The method may further include displaying at least a portion of theendocardium surface to include information about the frequencyrepresentation at corresponding locations of the endocardium surface.

The information about the frequency representation may includeinformation indicative of a dominant frequency in the frequencyrepresentation.

The method may further include using the determined physiologicalinformation to guide treatment of the heart cavity.

The treatment may include ablation of one or more selected regions ofthe heart.

The treatment may include cell therapy, gene therapy, or application ofother biological agents.

The method may further includes repeating the measurement of catheterelectrode signals and the determination of the physiological informationafter the treatment, and displaying a difference map includinginformation about how the determined physiological information changedin response to the treatment.

The determined physiological information may include isopotential linesor bands corresponding to sets of contiguous locations of the multiplelocations of endocardium surface having electrical potential values thatare equal or within a selected range. The method may further includedisplaying at least a portion of the endocardium surface to include atleast some of the isopotential lines. Displaying may include presentingthe isopotential lines for each of different phases of the heart beatcycle.

The determined physiological information may include electricalpotential values at the multiple locations of endocardium surface atdifferent phases of the heart beat cycle, and the method may furtherinclude displaying at least a portion of the endocardium surface toinclude information about the electric potential values. Displayinginformation about the electric potential values may include, forexample, the maximum electrical potential during the heart beat cyclefor different locations of the endocardium surface and/or the root meansquare of the electrical potentials during the heart beat cycle fordifferent locations of the endocardium surface.

The determined physiological information may include an activation timefor each of different locations of the endocardium surface. The methodmay further include displaying at least a portion of the endocardiumsurface to include representations indicative of the activations times.The activation times within a common activation time range are displayedwith a common color or visual indication.

The number of locations on the endocardium surface where physiologicalinformation is determined may be more than 10 times more than the numberof electrodes on the catheter.

The catheter may be spaced from the endocardium surface by more thanabout 3 mm when measuring the signals.

The method may further include displaying at least a portion of theendocardium surface to include at least some of the physiologicalinformation determined at the multiple surface locations.

The signals measured may be electrical signals. The signals measured maybe electric potential signals.

The physiological information may be electrical information. Thephysiological information may include electrical potentials at themultiple locations of the endocardium surface at each of one or morephases of the heart cycle, and any information derivable there from,such as: isopotential maps, maximum or RMS voltage maps, activation timemaps, and frequency maps.

Determining the position of the catheter electrodes may includemeasuring information about, for example, a position and/or orientationof the catheter within the heart cavity.

Determining the position of the catheter electrodes may be based furtheron information about the distribution of the electrodes on the catheter.Measuring information about, for example, a position and/or orientationof the catheter within the heart cavity may include measuring theposition of one or more catheter electrodes within the heart cavity.

Determining the position of the catheter electrodes may include directlymeasuring the position of each catheter electrode within the heartcavity.

In another aspect, a system is disclosed that includes a catheterconfigured to be inserted into a heart cavity having an endocardiumsurface, the catheter including multiple, spatially distributedelectrodes, the multiple electrodes configured to measure signals inresponse to electrical activity in the heart cavity with the catheterspaced from the endocardium surface. The system also includes aprocessing unit configured to determine physiological information atmultiple locations of the endocardium surface based on the measuredsignals and positions of the electrodes with respect to the endocardiumsurface.

In certain embodiments, the system may further include a sensor deviceconfigured to interact with the processing unit to determine thepositions of the catheter electrodes with respect to the endocardiumsurface.

Embodiments of the system may include any feature corresponding to anyof the features as set forth above for the method. For example, theprocessing unit can be configured (e.g., programmed) to carry out one ofmore of the processing/determining type method steps described above.

In a further aspect, disclosed is a computer program product residing ona machine-readable medium for storing computer instructions that, whenexecuted, cause a processor-based machine to receive from multiple,spatially distributed electrodes of a catheter, after the catheter hasbeen inserted into a heart cavity having an endocardium surface, signalsmeasured by the electrodes in response to electrical activity in theheart cavity, with the catheter spaced from the endocardium surface. Thecomputer instructions also cause the processor-based machine todetermine physiological information at multiple locations of theendocardium surface based on the measured signals and positions of theelectrodes with respect to the endocardium surface.

Like the system aspect, embodiments of the computer program product mayinclude any feature corresponding to any of the features as set forthabove for the method.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an exemplary non-contact mappingsystem.

FIG. 2 is a flow diagram of an exemplary non-contact mapping procedure.

FIG. 3 is an exemplary illustration of a representation of theendocardium surface.

FIG. 4 is an exemplary illustration of a meshed boundary representationof an endocardium surface of a left atrium.

FIG. 5 is an illustration of an exemplary voltage map generated using alinear color map matching scheme.

FIG. 6 is a flowchart of an exemplary embodiment of a catheterregistration procedure.

FIG. 7 is a flowchart of an exemplary embodiment of a procedure forreconstructing physiological information from signals acquired by themultiple electrodes of a catheter.

FIG. 8 is a flowchart of an exemplary embodiment of a procedure togenerate a resolution map.

FIG. 9 is a diagram showing a time and frequency representations of anexemplary electrogram.

FIG. 10 is a flowchart of an exemplary embodiment of a procedure togenerate an activation time map.

FIG. 11 is a flowchart of an embodiment of an exemplary embodiment of aprocedure to generate a voltage map.

FIG. 12 is a schematic diagram showing signal phase alignment.

FIG. 13 is a flowchart of an exemplary embodiment of a procedure forgenerating a frequency map.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Overview

Disclosed herein is a system and method for non-contact mapping andpresentation of physiological information in relation to an endocardiumsurface of a heart chamber. In certain embodiments, the non-contactmapping system uses a movable multi-electrode catheter that is displacedto multiple locations within the heart chamber, thereby improving theresolution and accuracy of data that can be acquired by a singlecatheter. Transformation functions are computed prior to thecommencement of signal measurement and acquisition of raw data toexpedite the reconstruction process to assemble the physiologicalinformation provided to a user.

FIG. 1 shows a schematic diagram of an exemplary embodiment of anon-contact system 100. The non-contact system 100 includes a moveablecatheter 110 having multiple spatially distributed electrodes. Duringthe signal acquisition stage of the non-contact mapping procedure thecatheter 110 is displaced to multiple locations within the heart chamberinto which catheter 110 is inserted.

In some embodiments the distal end of the catheter 110 is fitted withmultiple electrodes spread somewhat uniformly over the catheter. Forexample, the electrodes may be mounted on the catheter 110 following a3D olive shape. The electrodes are mounted on a device capable ofdeploying the electrodes into the desired shape while inside the heart,and retracting the electrodes when the catheter is removed from theheart. To allow deployment into a 3D shape in the heart, electrodes maybe mounted on a balloon, or shape memory material such as Nitinol.

At each of the locations to which the catheter 110 is moved, thecatheter's multiple electrodes acquire signals resulting from theelectrical activity in the heart cavity in a non-contact manner. Thus,at each of the locations to which the catheter 110 is moved, thecatheter is spaced from the endocardium surface. Consequently,reconstructing and presenting to a user (such as a doctor and/ortechnician) physiological data pertaining to the heart's electricalactivity may be based on information acquired at multiple locations,thereby providing a more accurate and faithful reconstruction ofphysiological behavior of the endocardium surface. The acquisition ofsignals at multiple catheter locations in the heart chamber enables thecatheter to effectively act as a “mega-catheter” whose effective numberof electrodes and electrode span is proportional to the product of thenumber of locations in which signal acquisition is performed and thenumber of electrodes the catheter has.

To enhance the quality of the reconstructed physiological information atthe endocardium surface, in some embodiments the catheter 110 is movedto more than three locations (for example, more than 5, 10, or even 50locations) within the heart chamber. Further, the spatial range overwhich the catheter is moved may be larger than one third (⅓) of thediameter of the heart cavity (for example, larger than 35%, 40%, 50% oreven 60% of the diameter of the heart cavity). Additionally, as will bedescribed in further details below, in some embodiments thereconstructed physiological information is computed based on signalsmeasured over several heart beats, either at a single catheter locationwithin the heart chamber or over several locations. In circumstanceswhere the reconstructed physiological information is based on multiplemeasurements over several heart beats, the measurements are synchronizedwith one another so that the measurement are performed at approximatelythe same phase of the heart cycle. The signal measurements over multiplebeats can be synchronized based on features detected from physiologicaldata such as surface ECG or intracardiac electrograms.

Non-contact mapping system 100 further includes the processing unit 120which performs several of the operations pertaining to the non-contactmapping procedure, including the reconstruction procedure to determinethe physiological information at the endocardium surface. As will bedescribed in more details below, the reconstruction of physiologicalinformation (e.g., electrical potentials) at the endocardium surfaceincludes the computations of transform functions corresponding to thesolution of the partial differential equations that define therelationship between acquired signals measured by the catheter'smultiple electrodes and the physiological behavior (e.g., the electricalpotential behavior) of the endocardium surface. We generally refer tothe “forward” transform or transformation as the mathematical operationthat provides the signals measured by the catheter electrodes based onthe electrical activity at different locations of the endocardiumsurface. Because there are typically many more endocardium surfacelocations than catheter electrodes this forward transformation isgenerally well-defined. We generally refer to the “inverse” transform assome type of mathematical inversion of this forward transformation toprovide information about the electrical activity at the differentlocations of the endocardium surface based on the measured signals atthe catheter electrodes. The reconstruction process also includes thecomputation of inverse transform functions based on a regularizationscheme to determine the physiological information at the endocardiumsurface based on the acquired signals at the multiple electrodes. Thus,the computational effort involved in the reconstruction process isconsiderable.

Accordingly, to expedite the computational operations performed by thenon-contact mapping system 100, the processing unit 120 can compute,generally prior to the insertion of the catheter into the heart chamberand/or before signal acquisition by the catheter's electrodes hascommenced, transformation functions that can be used in real-time tofacilitate the reconstruction process. More specifically, thetransformation functions that are applied to the raw data can berepresented in terms of individual transformation components. Theindividual transformation components include, for example,transformation components corresponding to chamber anatomy, and/or thegeometry of the catheter. Thus, to expedite the reconstruction procedurefor generating the endocardium surface's physiological information, theprocessing unit 120 computes transformation functions pertaining to thechamber's geometry and/or the catheter's geometry, and those componentsare combined during the reconstruction process with other transformationfunctions to form the overall inverse transform function.

Since the overall inverse transform that is applied to the raw datadepends on the particular location of the catheter 110 (for example,position and/or orientation) in the heart chamber, in some embodimentsthe reconstruction process can be further expedited by pre-computing theforward transform for multiple catheter and/or electrode locations.Because forward transforms have to be computed individually for thepossible catheter and/or electrode locations, the number of pre-computedforward transforms will be related to the number of possible locationsthat the catheter 110 may take in the heart chamber.

Once the catheter 110 is inserted and is displaced to a particularlocation in the heart chamber, the mapping procedure can be performedexpeditiously by computing in real-time those transformation componentsthat could not be computed ahead of the signal acquisition stage, andcombining those components with the appropriate pre-processedtransformation components to obtain the overall transformationfunction(s). That overall transformation function is applied to theacquired raw data to perform the inverse reconstruction operation.

In addition to computing the pre-processed partial transformationfunctions, the processing unit 120 also performs a catheter registrationprocedure. The location of the catheter 110 inserted into the heartchamber can be determined using a conventional sensing and trackingsystem (not shown) that provide the 3D spatial coordinates of thecatheter and/or its multiple electrodes with respect to the catheter'scoordinate system as established by the sensing and tracking system.However, to perform the mapping procedure and reconstruct physiologicalinformation on the endocardium surface, it is necessary to align thecoordinate system of the catheter 110 with the endocardium surface'scoordinate system. As will be described below in greater detail, theprocessing unit 120 (or some other processing module of system 100)determines a coordinate system transformation function that transformsthe 3D spatial coordinates of the catheter's locations into coordinatesexpressed in terms of the endocardium surface's coordinate system, orvice-versa.

As will become apparent below, the processing unit 120 also performspost-processing operations on the reconstructed physiologicalinformation to extract and display useful features of the information tothe operator of the system 100 and/or other persons (e.g., a physician).

As also shown in FIG. 1, the non-contact mapping system 100 includes theimage acquisition and preparation module 130. The acquisition andpreparation module 130 receives volumetric images (e.g., CT, MRI orultrasound images taken by a scanner apparatus) of the torso, andprocesses them, using a procedure referred to as segmentation, to obtaina representation of the endocardium surface. Mapping of the dataacquired by the multiple electrodes of catheter 110 is performed withreference to the representation of the endocardium surface. Once theboundary representation is constructed from the volumetric data, theboundary and or chamber volume representation is partitioned intoelements whose characteristics are determined in accordance with, amongother things, the types of numerical calculation techniques that areused to perform the mapping, as well as the overall geometry andcharacteristics of the endocardium surface as determined during thesegmentation process from the acquired volumetric images.

As further shown in FIG. 1, the signals acquired by the multipleelectrodes of catheter 110 are passed to the processing unit 120 via thesignal conditioning module 140. The signal conditioning module 140receives the signals communicated from the catheter 110 and performssignal enhancement operations on the signals before they are forwardedto the processing unit 120. Signal conditioning hardware is required toamplify, filter and continuously sample intracardiac potential measuredby each electrode. The intracardiac signals typically have a maximumamplitude of 60 mV, with a mean of a few millivolts. In some embodimentsthe signals are bandpass filtered in a frequency range (e.g., 0.5-500Hz) and sampled with analog to digital converters (e.g., with 15-bitresolution at 1 kHz). To avoid interference with electrical equipment inthe room, the signal can be filtered to remove the frequencycorresponding to the power supply (e.g., 60 Hz). Other types of signalprocessing operations such as spectral equalization, automatic gaincontrol, etc. may also take place. The resultant processed signals areforwarded by the module 140 to the processing unit 120 for furtherprocessing.

In some embodiments, the signal conditioning module 140 is implementedby use of integrated components on a dedicated printed circuit board. Inother embodiments, some of the signal conditioning tasks may beimplemented on a CPU, FPGA or DSP after sampling. To accommodate safetyregulations, the signal conditioning module is isolated from highvoltage power supplies.

The processing unit 120, image acquisition and preparation module 130shown in FIG. 1 is a processor-based device that includes a computerand/or other types of processor-based devices suitable for multipleapplications. Such devices can include volatile and non-volatile memoryelements, and peripheral devices to enable input/output functionality.Such peripheral devices include, for example, a CD-ROM drive and/orfloppy drive, or a network connection, for downloading related contentto the connected system. Such peripheral devices may also be used fordownloading software containing computer instructions to enable generaloperation of the respective unit/module, and for downloading softwareimplemented programs to perform operations in the manner that will bedescribed in more detailed below with respect to the various systems anddevices shown in FIG. 1. Alternatively, the various units/modules may beimplemented on a single processor-based platform capable of performingthe functions of these units/modules. Additionally or alternatively, oneor more of the procedures performed by the processing unit 120 and/orimage acquisition module 130 and/or signal conditioning module 140 maybe implemented using processing hardware such as digital signalprocessors (DSP), field programmable gate arrays (FPGA), mixed-signalintegrated circuits, etc. The signal conditioning module 140 istypically implemented using analog hardware augmented with signalprocessing capabilities provided by DSP, CPU and FPGA devices.

As further shown in FIG. 1, the non-contact mapping system 100 alsoincludes peripheral devices such as printer 150 and/or display device170, both of which are interconnected to the processing unit 120.Additionally, the non-contact mapping system 100 includes storage device160 that is used to store data acquired by the various interconnectedmodules, including the volumetric images, raw data measured byelectrodes and the resultant endocardium representation computed therefrom, the partially computed transformations used to expedite themapping procedures, the reconstructed physiological informationcorresponding to the endocardium surface, etc.

FIG. 2 is a flow diagram providing a top-level depiction of the variousprocedures performed by the system 100 in the course of performing thenon-contact mapping procedure 200. As shown, the system 100 initiallyacquires at 202 volumetric cardiac representations of a patient's heart.Such volumetric representations may include high-resolution CT, MRIand/or ultrasonic slice images providing data regarding the geometry andcharacteristic of the patient's heart. The volumetric data may beacquired in advance of the commencement of other procedures comprisingthe non-contact mapping procedure. For example, the volumetric data maybe obtained days or weeks prior to the catheter-insertion procedure. Thevolumetric data is initially received by the image acquisition andpreparation module 130 shown in FIG. 1, but may subsequently be storedfor future processing in storage module such as storage device 160.

Once volumetric data has been acquired, the image acquisition andpreparation module 130 uses the acquired data to segment the volume, at204, and provide a surface representation for a specific chamber intowhich the catheter will be inserted. The segmented volume is closed toallow numerical computations and partitioned to include surface elementswhose geometry and/or other characteristics are based on the geometry ofthe surface representation, the type of numerical computation procedureused in the to generate forward transform functions, etc. In someembodiments, multiple endocardium surface representations may begenerated, each of which corresponding to a different phase of theheart's cycle. For example, separate representations for the endocardiumsurface at multiple phases in the cardiac cycle such as systole and enddiastole may be generated. Subsequent reconstruction of physiologicalinformation may then be performed with respect to the appropriateendocardium surface representation, depending at what phase of the heartcycle raw data was acquired.

With the geometries of catheter and the endocardium surfacerepresentation having been determined or otherwise known, thenon-contact mapping procedure pre-computes at 206 transformationfunctions that thereafter can be quickly retrieved during thereconstruction of physiological information, thereby expediting thecomputation of the reconstruction transformations in real-time. Thepre-processed transformation functions are represented and stored in theform of look-up tables, in the form of matrices, functionalrepresentations, or the like. Individual transformation functionscorrespond to one or more of the multiple locations within the heartchamber to which electrodes and/or the moveable catheter 110 may bemoved during the non-contact mapping procedure. Other pre-computedtransformations may correspond to the various heart shapes with respectto which the physiological information is determined. The pre-computedtransformation functions may either be stored on a local storage moduleforming part of the processing unit 120, or may be alternatively storedon storage device 160.

The catheter 110 is inserted into the heart chamber to be studied at208. The catheter 110 is typically inserted into the heart chamber via asuitable blood vessel leading to the heart chamber. In some embodiments,the electrodes of the catheter 110 are bundled into a compactconfiguration that enables the catheter 110 to be delivered to the heartchamber with minimal obstruction. Once inside the heart chamber, theelectrodes of the catheter are deployed into a specified electrodearrangement relative to the catheter 110. During the mapping procedure200, the moveable catheter 110 is displaced to multiple locations withinthe heart chamber, whereupon the catheter's electrodes acquire andrecord signals (e.g., electrical signals) resulting from the electricalactivity of the heart.

As explained above, to reconstruct the physiological information of theendocardium surface, the system 100 applies reconstructiontransformations on the signals acquired by the multiple electrodes ofthe catheter 110. Because the transformations applied to the acquiredsignals depends, among other things, on the location of the catheterrelative to the endocardium surface, the mapping procedure firstestablishes the location of the catheter 110 with respect to theendocardium surface's coordinate system.

Accordingly, the non-contact mapping system 100 determines at 208 the 3Dlocation of the catheter in a 3D coordinate system that corresponds to asensing and tracking system used to locate the physical location of thecatheter 110. In some embodiments, the location of the electrodesrelative to the catheter 110 is fixed and known, and thus the onlyinformation that needs to be determined is the location and orientationof the catheter 110 in the 3D space established by the sensing andtracking system. Specifically, a sensor that is affixed to the catheter110 may be used to determine the location and orientation of thecatheter. In other embodiments the location and orientation of thevarious electrodes relative to the catheter may vary, and accordingly insuch embodiments sensors attached proximate to the various electrodesmay be used to facilitate the determination of the location of thecatheter and/or its electrodes or by using a scheme that localizeselectrode location using electrical impedance.

The sensing and tracking system employed to determine the 3D location ofthe catheter could be based, for example, on electromagnetic radiationtracking. In this method, electromagnetic fields are generated outsidethe patient body. A collection of miniaturized coils oriented to detectorthogonal magnetic fields forming a sensor are placed inside thecatheter to detect the generated magnetic fields. A processing unitdetermines the location and orientation of the sensor based on amplitudeand phase information in the signal detected by the multiple coils.Alternatively and/or additionally, the sensing and tracking system maybe based on ultrasound, impedance or fluoroscopy tracking. In impedanceand fluoroscopy tracking it is possible to locate the electrode locationwithout necessitating dedicated sensors. In the case of impedance,electrical potential generated by electric field generators are detectedby the existing electrodes. In case of fluoroscopy, electrode locationmay be detected by an image processing scheme that identifies and tracksthe electrodes and/or opaque markers located on the catheter.

As will be described in greater details below, to perform theregistration procedure, the 3D coordinates of the catheter 110 atvarious locations on the endocardium surface are determined. Thus, asshown at 210, an operator moves the catheter 110 inside the heartchamber until it determines that the catheter, or one or more of itselectrodes, touches the endocardium surface. The 3D spatial coordinatesof the catheter (and/or its electrodes) at that point on the endocardiumsurface is determined. The operator then moves the catheter 110 toadditional points on the endocardium surface, and the 3D coordinates ofthe catheter 110 relative to the sensing and tracking system employed,at those additional points on the endocardium surface are determined.

It will be appreciated that while the 3D spatial coordinates of thecatheter at various points on the endocardium surface relative to thesensing and tracking system's coordinate system are known, the identityof those points on the endocardium surface is not known. In other words,neither the identity nor the actual coordinates (with respect to theendocardium surface's coordinate systems) of the points at which thecatheter touches the endocardium surface are known.

Accordingly, to determine the identity of the endocardium surface pointscorresponding to the catheter's 3D spatial locations, and thus determinethe relationship between the endocardium surface's coordinate system andthe catheter's 3D coordinate system, the two coordinate system have tobe aligned. To align the two coordinate systems, a coordinate systemtransformation is computed at 212 that best matches the 3D locations ofthe catheter 110 at the endocardium surface, as determined at 210, topoints on the endocardium surface that was determined at 204. Putanother way, since the general geometry of the endocardium surface isknown (from the computation at 204), and since the 3D coordinates atseveral points where the catheter 110 touches the endocardium surfaceare likewise known, the alignment procedure determines the optimaltransformation that would cause the endocardium points expressed in thecatheter's 3D coordinate system to be congruent with the endocardiumsurface representation that was determined from the volumetric data.Optimization techniques, such as least-square error computationprocedures and/or other mathematical regression and curve-fittingtechniques, are used to align the catheter's 3D coordinate system andthe endocardium surface representation. In some embodiments thedetermination of the transformation function that aligns the twocoordinate systems can be based on as few as three (3) points on theendocardium surface with respect to which the 3D spatial coordinateshave been determined. More points may be used to compute thetransformation function, depending on the desired accuracy and theacceptable computation effort that may be undertaken.

In some embodiments, the catheter registration procedure to align theendocardium surface's coordinate system with the coordinate system usedto determine the 3D location of the catheter 110 yields a six (6)parameter transformation that includes three (3) displacement parameters(x₀, y₀, z₀) and three rotation parameters (θ₀, φ₀, ψ₀). Thesetransformation parameters are subsequently applied to 3D spatialcoordinates obtained by the sensing and tracking system used todetermine the catheter's spatial coordinates to obtain the catheter'slocations in terms of the endocardium surface's coordinate system.

Advantageously, generating a representation of the endocardium surfacebased on the pre-acquired volumetric data separately from determiningthe location of the catheter relative to the endocardium surfaceprovides an accurate representation of the endocardium surface notattainable by sequential contact mapping.

Once the registration procedure is performed, the catheter 110 is moved,at 214, to a first location within the heart chamber in which the firstset of measurement by the catheter's multiple electrodes is performed.Control of the catheter's movement and location within the heart chamberis performed manually by the operator manipulating the catheter 110.Alternatively, the movement of the catheter 110 within the heart chambermay be automated by use of techniques such as magnetic (see, e.g.,Stereotaxis, Inc. of St. Louis, Mo.) or robotic (see, e.g., HansenRobotics, Inc.) navigation. Catheter manipulation may be used to causethe catheter to follow a pre-determined displacement route to collectdata at locations that may be considered to be of higher interest thanothers. For example, in some embodiments the catheter 110 may be movedat specified displacement intervals in an area of the heart chamber thatis known have abnormal cardiac activity.

The 3D location of the catheter 110 is then determined using one of thetechniques discussed previously 110 and/or to its multiple electrodes,thereby providing the catheter's spatial location in the catheter'scoordinate system. The coordinates of the catheter 110 and/or itsmultiple electrodes relative to the endocardium surface (i.e., in theendocardium's coordinate system) are then computed using the coordinatesystem transformation function determined at 212.

At its current location, the multiple electrodes of the catheter 110acquire at 216 signals resulting from the heart's electrical activities.In some embodiments the signals are electrical signals (e.g., potential,current, magnetic, etc.).

The non-contact mapping system 100 generates at 218 reconstructiontransformation functions to be applied on the acquired signals toreconstruct the physiological information at the endocardium surface.The generated reconstruction transformation functions are based, amongother things, on the pre-computed reconstruction transformationfunctions that were determined at 206, and the catheter's locationrelative to the endocardium surface. Thus, in some embodiments, forevery location of the catheter 110 at which raw data is acquired, acorresponding set of reconstructed physiological information iscomputed.

As further shown in FIG. 2, after the raw data corresponding to theheart's electrical activity has been acquired, recorded and processedusing reconstruction transformation function(s) to obtain reconstructedphysiological information at the endocardium surface, a determination ismade, at 220, whether there are additional locations within the heartchamber to which the catheter 110 is to be moved. If there areadditional locations in the heart chamber to which the catheter 110needs to be moved the catheter is moved, using manual or automaticcontrol, to the next location in the heart chamber, whereupon theoperation described in relation to the blocks 214-218 in FIG. 2 areperformed for that next location.

Alternatively, in some embodiments, a composite resultant set ofphysiological information can be generated by selecting from multiplesets of reconstructed physiological information portions of thereconstructed information. As will become apparent below, selectingwhich portions of reconstructed information to use can be based onresolution maps that are indicative of the quality of the reconstructedinformation for a particular portion or set of the reconstructedphysiological information. Other criteria and technique for selectingsuitable portions of data to reconstruct a composite set ofphysiological information may be used.

Alternatively, in some embodiments, one (or more) compositereconstruction transformation function is computed that is appliedcollectively to the raw data acquired at multiple locations to generatea resultant composite set of reconstructed physiological informationbased on a substantial part of the data acquired. Such a transformationfunction represents a “mega transformation function” that corresponds tothe “mega catheter” referred to above, whose effective number ofelectrodes and electrode span is related to the number of locations towhich the catheter was moved within the heart chamber. Under thosecircumstances the generation of the composite reconstructiontransformation function is deferred until data is collected from thecatheter's multiple locations.

Alternatively, in some embodiments, the “mega transformation function”and “mega catheter” may be updated on an ongoing basis to take intoaccount a given relevant measurement window. This window may be a fixednumber of measurements such that the arrival of new measurementsdisplaces measurements that were obtained before the time window. Thisyields a constantly updating moving average.

In some embodiments, signals are measured throughout a heart beat cycle(for example, a measurement can be made at each catheter electrode ateach of multiple, different phases of a single beat heart cycle).

Yet in further embodiments the reconstructed set of physiologicalinformation is computed based on measurements taken over one or moreheart beats. In the latter situation, the catheter is moved to aparticular location, and acquires multiple sets of raw data over severalheart beats. The acquired data is averaged, and the reconstructionprocess is applied to the averaged values. As will become apparentbelow, if the data is acquired over B heart beats (i.e., Bmeasurements), an improvement in the signal-to-noise ratio proportionalto √{square root over (B)} is obtained. The timing of the measurementoperation is generally synchronized to ensure that measured data isacquired at approximately the same phase of the heart cycle.

If it is determined at 220 that there are no additional locations withinthe heart chamber at which data needs to be collected, then thenon-contact mapping system performs at 222 post-processing operations onthe reconstructed physiological information to extract clinically usefuldata. As noted, in some embodiments the non-contact mapping system 100produces a composite reconstructed set of physiological information.Post processing operation are performed, under those circumstances, onthe composite set of reconstructed physiological information. In somecircumstances where the non-contact mapping system 100 produces multiplereconstructed sets of physiological information for the raw datacollected at each location in the heart chamber to which the catheter110 was moved, the post processing operations are performed individuallyon one or more sets of reconstructed physiological information.

In some embodiments, the post processing may involve nothing furtherthen selecting a format for outputting (e.g., displaying) thereconstructed potentials to a user. In other embodiments, thepost-processing may involve significant further mathematicalmanipulation of the reconstructed potentials to provide additional typesof physiological information.

The reconstructed physiological information and/or sets ofpost-processed data are then displayed at 224. The information, be itthe reconstructed physiological information or any data resulting fromthe post-processing performed at 222, is displayed on a 3D graphicalrendering of the representation of the endocardium surface generated at204.

Some of the post-processing operations performed on the reconstructedset(s) of physiological information include the generation of aresolution map. Such a resolution map indicates the spatial resolutionof physiological information at points on the endocardium surface,thereby providing a measure of the reliability and accuracy of theinformation at various points on the endocardium surface. The resolutionmap may also be used to form a composite set of reconstructedphysiological information by associating with individual sets ofacquired raw data and/or individual sets of reconstructed physiologicalinformation corresponding resolution maps. A resultant composite set isthen formed by selecting portions of acquired raw data (or reconstructedinformation) whose reliability or accuracy, as indicated by theresolution map corresponding to the set from which the data is selected,is sufficiently high. Resolution maps may be used with any form ofpost-processing operation including all modes listed below. Strictlyspeaking, information about the resolution maps can be determined priorto obtaining the reconstructed potential data; however, herein wegenerally refer to the generation and display of the resolution map as“post-processing” because such information is typically presented to theuser after at least some of the potentials are reconstructed.

Another type of post-processing operation that may be performed includesthe generation of isopotential maps. Particularly, where thereconstructed physiological information pertains to electricalpotentials, the reconstructed potentials may be color coded andsuperimposed on the 3D endocardial representation. Isopotential maps arethe reconstructed potentials computed for every sampled set of data overa single or multiple heart beats.

Yet another type of post-processing operation includes the generation oftiming maps (such as activation time maps). The timing maps provideinformation on the time-dependent behavior of the heart's electricalactivity. Particularly, the activation map indicates at what point intime particular points on the endocardium surface experience a change intheir electrical activity. For example, the activation map couldidentify the point in time at which particular cells on the endocardiumsurface experienced depolarization. Another type of timing map may be aniso-duration map where the amount of time certain tissue has been activefor is detected. Timing maps may be computed from the reconstructedpotentials over a single or multiple heart beats. Timing maps may bedetermined and displayed for one or more points on the endocardiumsurface representation.

Another type of post processing operation that may be performed at 222is the generation of voltage maps. Voltage maps can be used to displaycharacteristics of voltage amplitude in a given area. The voltage mapsmay be computed from the reconstructed potentials over a single ormultiple heart beats. Useful voltage map information that may bedetermined and displayed for one or more points on the endocardiumsurface representation includes the maximum amplitude, or root meansquare potential values.

Another type of post-processing operation is the generation of adifference map. The difference map provides information regarding theeffectiveness of the clinical procedure (e.g., ablation) performed onthe patient to ameliorate the symptoms of arrhythmias. The differencemap compares the electrical behavior of the heart, as reflected from twoor more voltage maps generated before and after the performance of theparticular clinical procedure.

A further type of post processing operation is the generation offrequency maps. Frequency mapping, and more generally spectral analysis,are used to identify on the endocardium surface localized sites ofhigh-frequency activity during fibrillation. Frequency maps are computedby acquiring multiple sets of reconstructed information over aparticular time interval which includes a single or multiple heartbeats. The acquired raw data is then used to obtain the frequencyrepresentation of that data. Specific information (e.g., dominantfrequency components) from the frequency representation is subsequentlyidentified, and that identified information may be displayed.

Other types of post-processing information may likewise be performed at222.

The various procedures described above will now be described in greaterdetail.

Boundary Construction Procedure

As noted above, physiological information is reconstructed for anendocardium surface representation that is generated from pre-acquiredvolumetric data obtained using such techniques as coherence tomography(CT) imaging, magnetic resonance imaging (MRI) techniques, and/orultrasound-based imaging technique. The volumetric data can be obtainedin advance of the performance of the signal acquisition and/or thereconstruction procedure, or it can be obtained substantiallyconcomitantly with the performance of either of these procedures. Asdescribed in further detail below, in preferred embodiments, forexample, volumetric data, represented as image slices, can be acquiredbefore the catheter 110 is inserted into the heart chamber that is to bemapped.

Acquisition of the volumetric data is performed using conventionalscanning apparatus, such as CT, ultrasound, or MRI scanners, thatprovide acquired volumetric images to the image acquisition andpreparation module 130. In preferred embodiments, the acquired image isusually stored and transferred in industry standard, e.g., DICOM format.

In order to facilitate the boundary construction procedure, thevolumetric images may be acquired under a customized protocol. Tofacilitate the identification and construction of the endocardiumsurface (sometimes referred to as the blood-to-endocardium-boundary)from the acquired volumetric images, contrast agents may be injectedinto the body during image acquisition. The injection is timed such thatthe contrast agent is present in the endocardium during the acquisitiontime.

Since the heart contracts during imaging, additional physiologicalinformation is recorded and incorporated with image data duringacquisition time. Parameters such as EKG and respiration phase enablematching the volumetric images to the specific phase of the heart andrespiratory cycle.

To generate a representation of the endocardium surface from highresolution volumetric data, one first retrieves the volumetric dataeither from the image acquisition and preparation module 130 or fromstorage device 160. While the pre-acquired volumetric images are of highquality, in their original form they do not provide explicit informationabout the endocardium boundary. Accordingly, a boundary representationof the endocardium surface is generated based on the volumetric datausing a procedure known as segmentation.

The segmentation algorithm detects the blood to endocardium boundaryusing the difference in corresponding contrast enhanced by the injectedcontrast agent. The segmentation may be performed utilizing one of anumber of algorithms. One such algorithm is seeded region growing. Thebasic approach of the algorithm is to start from a seed region(typically one or more volume pixels, denoted voxels) that areconsidered to be inside the object to be segmented. The voxelsneighboring this region are evaluated to determine if they should alsobe considered part of the object. If so, they are added to the regionand the process continues as long as new pixels are added to the region.

The evaluation criteria for inclusion as part of the region may bebased, for example, on algorithm based on statistical properties of theregion. First, the algorithm computes the mean and standard deviation ofintensity values for all the voxels currently included in the region. Auser-provided factor is used to multiply the standard deviation anddefine a range around the mean (for example 3×). Neighbor voxels whoseintensity values fall inside the range are accepted and included in theregion. When no more neighbor voxels are found that satisfy thecriterion, the algorithm is considered to have finished its firstiteration. At that point, the mean and standard deviation of theintensity levels are recomputed using all the voxels currently includedin the region. This mean and standard deviation defines a new intensityrange that is used to visit current region neighbors and evaluatewhether their intensity falls inside the range. This iterative processis repeated until no more voxels are added or the maximum number ofiterations is reached.

A number of segmentation techniques may be used to perform segmentation.A number of commercially available and open source segmentation toolsmay be used to perform boundary detection. For example, these includeCardiac++ by GE Healthcare systems, Amira by Mercury Computer Systems,and the open source National Library of Medicine Insight Segmentationand Registration Toolkit (ITK).

FIG. 3 provides an exemplary illustration of a resultant boundaryrepresentation of the endocardium surface produced from the acquiredhigh resolution volumetric data after the performance of segmentation.As shown in FIG. 3, representative volumetric data comprising four imageslices of the heart cavity are processed using a segmentation techniqueto generate the 3D boundary representation of the endocardium surface.

Once the endocardium representation has been obtained from thevolumetric data, the anatomical geometry of the endocardiumrepresentation is partitioned into a discrete number of surface elementsto facilitate the performance of the numerical computations that have tobe undertaken during the reconstruction process. The number of surfaceelements, as well as their geometry, controls the maximum attainableresolution of the eventual reconstructed physiological information.

The partitioning of the segmented endocardium surface representationdepends, among other things, on the numerical computation method used tosolve the relevant partial differential equations (PDE's) that definethe relationship between the physiological information at theendocardium surface and the measured signals. For example, finitedifferences (FD) and finite volume numerical methods, as well as theimmersed boundary adaptation of the finite difference numerical method,use regular Cartesian grids. The finite element Method (FEM) numericalmethod uses volumetric meshes, often built from tetrahedra. On the otherhand, the boundary element method (BEM), which is based on integralequations, uses a surface mesh generally comprising triangle-shapedsurface elements, although higher order elements, such as splines, maysimilarly be used. It is to be noted that an advantage of the BEM methodis that implementation of the BEM method to perforin the computationneeded to solve the PDE's in the course of the reconstruction processresults in the generation of a non-varying mesh that does not change fordifferent catheter positions.

In some embodiments the endocardium surface representation obtained ispartitioned by using the Strang-Persson approach. Briefly, the approachis based on a mechanical analogy of a stable equilibrium trussconsisting of weights and springs. The method assumes that the boundaryis supplied as a signed distance function. A signed distance functionrefers to a three dimensional grid of data that contains the values ofthe function phi that represents the signed distance (<0 if inside, >0if outside) from a given point to the boundary. The signed distancefunction may be readily obtained following the segmentation processdescribed above.

The Strang-Persson approach places a number of nodes inside the domainand imagines that there is a compressed string along each edge that iscreated by Delaunay triangulation. The method then allows each spring torelax to its equilibrium length insomuch as it is constrained by otherstrings and as long as no node goes outside of the domain. If the nodestravel a significant distance, then Delaunay triangulation is repeated.This procedure is considered to be robust and produces high qualitymeshes.

It will be appreciated that other partitioning techniques may be used topartition the endocardium surface representation.

One problem that remains after the segmentation and partitioningprocedures described above has been performed is the existence ofgeometrical discontinuities in the generated meshed representation ofthe endocardium surface. Elliptic equations, which correspond to a broadcategory of PDE's that includes the Laplace and Poisson equations, aresolved with respect to closed domains for which a boundary condition isspecified with respect to every point on the boundary. If boundaryconditions are not specified along some part of the boundary, thesolution for the PDE's is not uniquely defined. As a result, in additionto the correct solution there will be incorrect solutions for theparticular PDE's to be solved that will satisfy the specifiedconditions. This property of elliptic equations (i.e., that undefinedboundary conditions may yield incorrect solutions) is inherent to anynumerical method. Thus, in some embodiments, the geometry of theendocardium surface is closed. Thus, for example, when applying the BEMnumerical method using triangle-shaped surface elements, the endocardiumsurface, as well as the surface of the catheter, have to be representedby meshes having no gaps or overlaps.

Accordingly, having generated the partitioned endocardium surfacerepresentation, all openings on the representation (e.g., arteries orveins extending from the endocardium surface) are typically closed. Theopening in the geometry of the endocardium representation can be closedwith surface sections providing the shortest distances between any givenpoint along the perimeters of the openings on the endocardium surfacerepresentation. Alternatively, geometries satisfying different criteriafor the sections used to patch the openings in the endocardium surfacerepresentation may be used. Additionally, the sections that have beenadded to the representation where opening previously existed arepartitioned to surface elements having the same geometry as the surfaceelements used in endocardium surface representation. Thus, inembodiments where the Strang-Persson approach had been used to partitionthe endocardium surface into a mesh with triangle-shaped surfaceelements, the surface sections now covering the openings also includetriangle-shaped surface elements. Subsequently, the solutions of thePDE's that will result in the reconstructed physiological information atthe endocardium surface will include information pertaining to the addedsections covering the opening. However, when presenting thephysiological information to a user, or when displaying a graphicalrendering of the endocardium surface representation, the added sectionscovering the actual openings in the surface should be excluded.

Accordingly, upon performing the boundary construction procedure, aresultant meshed representation of the endocardium surface is produced.That meshed representation of the endocardium surface satisfies thefollowing requirements:

-   -   1. Closed Surface—as mentioned above, all surfaces must be        closed. Prior segmentation techniques produce meshes that fully        represent the veins and arteries. For the purposes of        implementing numerical techniques, the veins and arteries must        be effectively closed off with high quality meshes. The closed        surfaces covering the veins and arteries in the derived        representation of the endocardium surface may be subsequently        removed prior to displaying the reconstructed physiological        information or prior to otherwise making any use with the        reconstructed physiological representation    -   2. Element Number—the number of surface elements should be high        enough to resolve the geometry but, on the other hand, to the        extent that the resultant geometry is adequately resolved, be as        coarse as possible to facilitate speedy numerical computations.        Endocardium surface representation that include too many surface        elements (e.g., triangles) to accommodate unimportant local        features of the geometry should be avoided.    -   3. Element Quality—The surface elements should be of high        quality. The quality of a triangle-shaped surface element, for        example, is a measure of how close it is to being equilateral. A        simple measure of the triangle quality is the “radius ratio”.        The radius ratio is defined as the ratio of the radius of the        circumscribed circle and that of the inscribed circle. The        smaller the value, the higher the quality of the triangle. An        equilateral triangle yields a radius ratio of 2, which is the        lowest attainable value. The quality of the entire mesh can be        measured as the average quality of the individual surface        elements. However, some numerical computation techniques, such        as FEM, are known to be sensitive to the quality of the worst        triangle. It will be appreciated that the other quality metrics,        pertaining to different types of surface element geometry may be        used.

FIG. 4 is an exemplary illustration of a resultant meshed boundaryrepresentation of an endocardium surface of a left atrium generated byperforming the procedure described herein. As shown, the boundaryrepresentation of the left atrium includes a mesh of triangular surfaceelements.

As described herein, physiological data obtained during the acquisitionof the volumetric data may be used to enable the matching of volumetricimages to the specific phase of the heart's mechanical cycle. Inaddition, in some embodiments, reconstructed physiological informationobtained by performing of the non-contact mapping procedure could bepresented on endocardium surface representation that most closelymatches the mechanical phase of the heart during which the raw dataresulting from the heart's electrical activity was acquired. Thus, inthose embodiments, separate representations of the endocardium surfacecan be generated for different phases of the heart's cycle using theboundary construction procedure. Raw data acquired during a particularphase would then be reconstructed with respect to the representation ofthe endocardium surface corresponding to that phase. As describedherein, the reconstruction transformations and catheter registrationprocedure are performed with respect to that particular representationof the endocardium surface. Similarly, the reconstructed physiologicalinformation is subsequently displayed using the correspondingendocardium surface representation. In circumstances where the raw datais acquired at a phase that does not have a corresponding endocardiumsurface representation, the reconstruction operations will be performedusing the closest matching endocardium surface representation.

Pre-Computed Transformation Functions

As explained above, some of the computations involved in computing thereconstruction transformation are complex and time consuming. It istherefore preferable not to perform all of them in the clinical settingwhere physician interaction is required.

Because endocardial boundary and catheter geometries are known inadvance, it is possible to perform computations corresponding to thereconstruction procedure, for example, computations pertaining to theforward transform, before the reconstruction procedure is performed, andstore the pre-computed transform functions in memory for later use. Whenperforming the reconstruction procedure, the pre-computed transformationfunctions can be retrieved from storage and used to compute the overallforward transform, thereby expediting the computation of the overallforward transform in real-time.

The reconstructed sets of physiological information are the solutions toelliptic partial differential equations (PDE's) that arise fromLaplace's equation. To solve an elliptic PDE, a multi-variable functionis defined on the closed domain that satisfies two properties: the bulkequation inside the domain (in the present case, e.g., blood) and theboundary conditions (in the present case, e.g., the endocardium andcatheter).

Conventional numerical techniques used to compute solutions for PDE'sconvert PDE problems into a set of algebraic equations. Some componentsof these algebraic equations depend only on the geometry and the natureof the bulk equation, and others components depend on the boundaryconditions. While the boundary conditions are not necessarily knownduring the pre-computation phase, much is known about the geometry. Forexample, the shape of the heart's cavity and the shape of the catheterare both known in advance of the signal measurement and reconstructionprocesses. Therefore, components of the transformation functions (or insome circumstances, the complete transform functions) are computed priorto the acquisition of raw data and/or reconstruction of physiologicalinformation from that raw data. Subsequently, these pre-computedtransformation function components are used to expedite the computationof the full transformation functions. In circumstances where fullreconstruction functions had been computed for particular locations ofthe catheter and/or electrodes 110, the computation of the forwardtransformation functions in real-time is avoided.

The pre-computed transformation functions can be used with any type ofnumerical computation methods used to solve the PDE's to obtain thereconstructed physiological information solutions. The following exampleillustrates how pre-computed transformation functions can be determinedand used in conjunction with boundary element method (BEM) for solvingthe corresponding PDE's. It will be appreciated that similarmathematical frameworks can be developed for other numerical computationmethods, and that generation of pre-computed transformation functionsfor use with those other numerical computation methods can likewise beperformed.

The BEM method is based on Green's second identity, as provided below:

$\begin{matrix}{{{\int_{S}\ {\mathbb{d}{S\left( {{U\frac{\partial V}{\partial n}} - {V\frac{\partial U}{\partial n}}} \right)}}} = {\int_{\Omega}\ {\mathbb{d}{\Omega\left( {{U{\nabla^{2}V}} - {V{\nabla^{2}U}}} \right)}}}},} & (1)\end{matrix}$where V and U are two functions defined on the domain Ω with boundary S.The operator ∂/∂n is the normal derivative with respect to the outwardnormal and ∇² is the Laplace operator (the so-called “Laplacian”). Theabove equation applies to an arbitrary domain, including domains withcavities. In the problem with a balloon catheter, Ω is the domainoccupied by blood and S is the union of the (artificially closed-off)endocardium and the surface of the catheter.

The variable V represents the electrostatic potential, which has theproperty that its Laplacian vanishes. The variable U can be defined as

$U = \frac{1}{r}$with the distance r measured from a given origin o. The Laplacian of Ucan thus be represented as:

$\begin{matrix}{{\nabla^{2}\frac{1}{r}} = {{- 4}{{{\pi\delta}_{o}\left( {x,y,z} \right)}.}}} & (2)\end{matrix}$where δ_(o)(x, y, z) is the three-dimensional Delta function at theorigin o. Substituting the relationship represented in Equation (2) forU in Green's second identity, as shown in Equation (1), yields thefollowing identity which is valid at all interior points of Ω:

$\begin{matrix}{V = {\frac{1}{4\pi}{\int_{S}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V}{\left. {\partial n} \right|}} - {V\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}}} & (3)\end{matrix}$where r is the distance from the point at which V is being evaluated toeach of the points on the surface. Similarly, the following relationshipis valid for points on the boundary:

$\begin{matrix}{V = {\frac{1}{2\pi}{\int_{S}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V}{\partial n}} - {V\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}}} & (4)\end{matrix}$

The variable S_(e) is defined as the surface of the heart, and thevariable S_(e) is defined as the surface of the catheter. Thus, usingthis definition, the overall surface S, with respect to which equation(4) is to be solved, is defined as the union of the heart's surface andthe catheter's surface, or S=S_(e) ∪S_(c). Both surfaces are assumedclosed and sufficiently smooth. Similarly, the variable V_(e), andcorrespondingly

$\frac{\partial V_{e}}{\partial n}$are defined as the potential and its normal derivative on the surface ofthe heart. V_(c) and

$\frac{\partial V_{c}}{\partial n}$are likewise defined as the potential and its normal derivative on thesurface of the catheter. It will be appreciated that solving the PDE'swith respect to surface potential is for illustrative purposes only, andthat Green's second identity relationship may be solved for other typesof physiological information.

In the forward problem, V_(e) is subject to Dirichlet boundaryconditions on S_(e) and zero Neumann boundary conditions on S_(c) in thecase where the catheter displaces a significant amount of blood.Accordingly, V_(e) and V_(c) can be determined using the following pairof equations:

$\begin{matrix}{V_{e} = {{\frac{1}{2\pi}{\int_{S_{e}}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V_{e}}{\partial n}} - {V_{e}\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}} + {\frac{1}{2\pi}{\int_{S_{c}}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V_{c}}{\partial n}} - {V_{c}\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}}}} & (5) \\{V_{c} = {{\frac{1}{2\pi}{\int_{S_{e}}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V_{e}}{\partial n}} - {V_{e}\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}} + {\frac{1}{2\pi}{\int_{S_{c}}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V_{c}}{\partial n}} - {V_{c}\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}}}} & (6)\end{matrix}$

While Equations (5) and (6) appear identical, in each case the variabler is the distance from the point at which V_(e) or V_(c) is beingevaluated to each point on either S_(c) or S_(e). In other words, thevariable r assumes, for each of the equations, different values duringthe evaluation of the respective integrals, thus resulting in differentvalues for V_(e) and V_(c). Since

$\frac{\partial V_{e}}{\partial n} = 0$these equations simplify to

$\begin{matrix}{V_{e} = {{\frac{1}{{2\pi}}{\int_{S_{e}}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V_{e}}{\partial n}} - {V_{e}\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}} + {\frac{1}{2\pi}{\int_{S_{e}}\ {\mathbb{d}{S\left( {{- V_{e}}\frac{\partial\frac{1}{r}}{\partial n}} \right)}}}}}} & (7) \\{V_{c} = {{\frac{1}{2\pi}{\int_{S_{c}}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V_{c}}{\partial n}} - {V_{c}\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}} + {\frac{1}{2\pi}{\int_{S_{c}}\ {\mathbb{d}{S\left( {{- V_{c}}\frac{\partial\frac{1}{r}}{\partial n}} \right)}}}}}} & (8)\end{matrix}$

Suppose that both surfaces are represented by irregular meshes and thatV_(e) and V_(c) are now discrete BEM approximations to the truepotential (or other physiological characteristics), subject totruncation errors as well as round off errors. The integration operationcan be replaced by discrete operators S_(e→e), O_(c→e), S_(c→e),O_(e→e), S_(e→c), O_(e→e), S_(c→c), O_(c→e). Mnemonically, “S” standsfor “Solid angle”, “O” stands for “One-over-r” and the subscripts referto either endocardium surface (“e”) or the catheter surface (“c”). Thesediscrete operators, which approximate the integration operationperformed to determine the potentials at the respective surfaces canthus be represented as matrices. For a collocation-type BEM in which thepotential is assumed to be constant over an entire element (e.g., asurface element on the particular representation of the surface used),the contribution from the operator S_(e→e) to the potential at thei^(th) element of the endocardium surface based on the j^(th) element onthe endocardium surface can be computed as:

$\begin{matrix}{{\left( S_{e\rightarrow e} \right)_{ij} = {{- \frac{1}{2\pi}}{\int_{\Delta_{j}}{\frac{\partial\frac{1}{r_{ij}}}{\partial n}\ {\mathbb{d}S}}}}},} & \left( {9a} \right)\end{matrix}$where the integration takes place over the j^(th) element, and r_(ij) isthe distance from a given point on the j^(th) element to the center ofthe i^(th) element. Similarly, the other operators can be expressed asfollows:

$\begin{matrix}{{\left( O_{e\rightarrow e} \right)_{ij} = {\frac{1}{2\pi}{\int_{\Delta_{j}}{\frac{1}{r_{ij}}\ {\mathbb{d}S}}}}},} & \left( {9b} \right)\end{matrix}$where the i^(th) and j^(th) elements are on the endocardium surface,

$\begin{matrix}{{\left( S_{c\rightarrow e} \right)_{ij} = {{- \frac{1}{2\pi}}{\int_{\Delta_{j}}{\frac{\partial\frac{1}{r_{ij}}}{\partial n}\ {\mathbb{d}S}}}}},} & \left( {9c} \right) \\{{\left( O_{c\rightarrow e} \right)_{ij} = {\frac{1}{2\pi}{\int_{\Delta_{j}}{\frac{1}{r_{ij}}\ {\mathbb{d}S}}}}},} & \left( {9d} \right)\end{matrix}$where the i^(th) element is on the endocardium surface and the j^(th)element is on the catheter surface,

$\begin{matrix}{{\left( S_{e\rightarrow c} \right)_{ij} = {{- \frac{1}{2\pi}}{\int_{\Delta_{j}}{\frac{\partial\frac{1}{r_{ij}}}{\partial n}\ {\mathbb{d}S}}}}},} & \left( {9e} \right) \\{{\left( O_{e\rightarrow c} \right)_{ij} = {\frac{1}{2\pi}{\int_{\Delta_{j}}{\frac{1}{r_{ij}}\ {\mathbb{d}S}}}}},} & \left( {9f} \right)\end{matrix}$where the i^(th) element is on the catheter surface and the j^(th)element is on the endocardium surface, and

$\begin{matrix}{{\left( S_{c\rightarrow c} \right)_{ij} = {{- \frac{1}{2\pi}}{\int_{\Delta_{j}}{\frac{\partial\frac{1}{r_{ij}}}{\partial n}\ {\mathbb{d}S}}}}},} & \left( {9g} \right) \\{{\left( O_{c\rightarrow c} \right)_{ij} = {\frac{1}{2\pi}{\int_{\Delta_{j}}{\frac{1}{r_{ij}}\ {\mathbb{d}S}}}}},} & \left( {9h} \right)\end{matrix}$where the i^(th) and j^(th) elements are both on the catheter surface,

The integral equations above can be converted into the finite linearsystem

$\begin{matrix}{V_{e} = {{S_{e\rightarrow e}V_{e}} + {O_{e\rightarrow e}\frac{\partial V_{e}}{\partial n}} + {S_{c\rightarrow e}V_{c}}}} & (10) \\{V_{c} = {{S_{e\rightarrow c}V_{e}} + {O_{e\rightarrow c}\frac{\partial V_{e}}{\partial n}} + {S_{c\rightarrow c}V_{c}}}} & (11)\end{matrix}$

Combining like terms yields:

$\begin{matrix}{{{\left( {S_{e\rightarrow e} - I} \right)V_{e}} + {O_{e\rightarrow e}\frac{\partial V_{e}}{\partial n}} + {S_{c\rightarrow e}V_{c}}} = 0} & (12) \\{{{S_{e\rightarrow c}V_{e}} + {O_{e\rightarrow c}\frac{\partial V_{e}}{\partial n}} + {\left( {S_{c\rightarrow c} - I} \right)V_{c}}} = 0} & (13)\end{matrix}$where I is the identity operator.

In the above system, for the forward transformation, V_(e) is knownwhile

$\frac{\partial V_{e}}{\partial n}$and V_(c) are unknown. The system can be rewritten in block form as

$\begin{matrix}{{\begin{bmatrix}O_{e\rightarrow e} & S_{c\rightarrow e} \\O_{e\rightarrow c} & {S_{c\rightarrow c} - I}\end{bmatrix}\begin{bmatrix}\frac{\partial V_{e}}{\partial n} \\V_{c}\end{bmatrix}} = \begin{bmatrix}{\left( {I - S_{e\rightarrow e}} \right)V_{e}} \\{{- S_{e\rightarrow c}}V_{e}}\end{bmatrix}} & (14)\end{matrix}$

The vector

$\frac{\partial V_{e}}{\partial n}$can be eliminated from the above system, leaving the followingexpression for V_(c):V _(c)=(−S _(e→c) +I+O _(e→c) O _(e→e) ⁻¹ S _(e→e))⁻¹(O _(e→c) O _(e→c)⁻¹(I−S _(e=e))+S _(p→c))V _(e)  (15)

The operators, or matrices, applied to V_(e) form the so-called forwardtransformation, denoted A, that relates the potentials at theendocardium surface, resulting from the electrical activity of theheart, to the potential measured at the multiple electrodes of thecatheter 110. In order to provide the transformation from catheter toendocardial potentials, we must interpret A as an equation for{circumflex over (V)}_(e) given V_(c). Because matrix A is generallyrectangular, underdetermined, and rank deficient to within IEEE doubleprecision, reconstructing {circumflex over (V)}_(e) correctly throughdirect inversion is difficult because this equation allows infinitelymany solutions, all but one of which are incorrect. Instead, some typeof regularization technique is used to incorporate a priori knowledgeabout the system to better specify the correct solution. Theregularization technique may include mathematical smoothing, statisticalmethods, and/or iterative techniques, such as conjugate gradientmethods. Further below, a Tikhonov regularization technique is describedfor solving for {circumflex over (V)}_(e); however, such a technique isby no means limiting.

With reference again to Equation (15), several of the matrices that formthe forward transform A are pre-computed prior to the reconstructionoperations. For example, the matrix S_(c→c) depends only on the geometryof the catheter and can therefore be pre-computed. If the catheter canassume different shapes and configurations, separate S_(c→c) matricescorresponding to each such shape/configuration are computed. Thematrices S_(e→e), O_(e→e) and O_(e→e) ⁻¹ depend on the geometry of theheart and can be pre-computed if the shape of the heart is obtainedbefore the procedure. In some embodiments O_(e→e) is the largest matrixsince the heart is represented by a mesh with significantly moreelements than the catheter. For example, the heart is typicallyrepresented by 3000 triangles while the catheter is typicallyrepresented by 500. Therefore, O_(e→e) is a 3000×3000 matrix, and thusits inversion would be costly (especially in terms of time) if theinversion had to be performed in real-time. The matrix computed as−S_(c→e)+I+O_(e→c)O_(e→e) ⁻¹S_(c→e), on the other hand, has a size of500×500 elements and its inversion, therefore, could be performed morequickly.

The matrices corresponding to the independent geometry of theendocardium surface and/or the catheter are computed in advance of thesignal acquisitions and/or reconstruction stages of the non-contactmapping. These pre-computed matrices are stored in a memory device, suchas storage device 116 for later retrieval. After raw data had beenacquired, the forward transform matrix is generated by retrieving, forexample, the pre-computed S_(e→e), O_(e→e) and O_(c→e) ⁻¹ matrices, andusing them in the course of computing the full forward transform matrix.In these embodiments where at least some of the pre-computed componentsassociated with the forward transform are available, the forwardtransform matrix has effectively been partially pre-computed.

Unlike the matrices S_(e→e), O_(e→e) and O_(c→e) ⁻¹ and S_(c→c), theother matrices depend on the configuration of the endocardium and thecatheter 110 relative to each other (e.g., the relative distance betweenthe catheter and specific locations on the endocardium surface).Although the relative positions and configuration of the endocardiumsurface relative to the catheter are typically known only when thecatheter is inserted into the heart chamber and the data acquisitionprocess has begun, to further expedite the reconstruction procedure, insome embodiments, the following matrix may be pre-computed for a largenumber of possible catheter locations:(−S _(c→c) +I+O _(e→e) O _(e→e) ⁻¹ S _(c→e))⁻¹(O _(e→c) O _(e→e) ⁻¹(I−S_(e→e))+S _(e→e))  (16)

Thus, for various discrete locations of the catheter 110 relative to theendocardium surface, the overall matrix expressed by Equation (16) canbe independently computed and stored at storage device 160 (orelsewhere) for subsequent retrieval. During the performance of themapping procedure, the catheter's position relative to the endocardiumsurface is determined and rather than computing the matrix of Equation(16) from scratch, the location is used to access a look-up table toretrieve the appropriate pre-computed matrix corresponding to thedetermined location.

The practicality of computing full forward transforms depends, to anextent, on the type of catheter used. For example, balloon-typecatheters are configured in such a way that their movement in the heartchamber causes enough of the blood occupying the heart chamber to bedisplaced that a significant change in potential distribution occurs. Asa result, for every variation of the position and orientation of aballoon-type catheter inside the heart chamber there will be a differentforward transform associated with that position/orientation. The effortof pre-computing forward transforms for various positions/orientationsof a balloon-type catheter then becomes proportional to the product ofthe number of possible spatial positions and the number of rotationalconfiguration at those positions.

On the other hand, for some types of catheters, for examplebranch-shaped catheters (or otherwise porous, hollow catheters), theextent of blood displacement in the heart chamber is much lesssignificant than for balloon-shaped catheters. Accordingly, one canapproximate the presence of the catheter as having no effect on theelectrostatic potentials inside the heart cavity, and essentially theintegrations over the catheter surface in Equations (7) and (8), and theequations derived there from, can be ignored. As a result, the catheterorientation is no longer required to calculate the forward transformmatrix A, the only knowledge that is required regarding the catheter isthe positions of the various catheter electrodes.

For the type of catheters that do not displace a significant amount ofblood the solution V to Laplace's equation at any interior point of Ω isgiven by

$V = {\frac{1}{4\pi}{\int_{S_{e}}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V_{e}}{\partial n}} - {V_{e}\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}}$

where V_(e) is specified by Dirichlet boundary conditions and

$\frac{\partial V_{e}}{\partial n}$is determined from the integral equation

$V_{e} = {\frac{1}{2\pi}{\int_{S_{e}}\ {\mathbb{d}{S\left( {{\frac{1}{r}\frac{\partial V_{e}}{\partial n}} - {V_{e}\frac{\partial\frac{1}{r}}{\partial n}}} \right)}}}}$or, in the matrix form

$V_{e} = {{O_{e\rightarrow e}\frac{\partial V_{e}}{\partial n}} + {S_{e\rightarrow e}V_{e}}}$which means that

$\frac{\partial V_{e}}{\partial n}$can be computed from V_(e) by only applying matrices O_(e→e) and S_(e→e)which can be entirely pre-computed:

$\frac{\partial V_{e}}{\partial n}{O_{e\rightarrow e}^{- 1}\left( {I - S_{e\rightarrow e}} \right)}V_{e}$

Importantly, this means that theoretically the forward operator can bepre-computed for finding the solution to Laplace's equation at any pointin the interior of the heart. In reality, the forward operator needs tobe pre-computed for a sufficient number of interior points so that anaccurate solution at any interior point can be computed by a simpleinterpolation.

In general, for all types of catheters, significant benefits can bedrawn from pre-computing the forward operator for a number ofstrategically selected catheter locations. When the physical catheter isfound near a pre-computed location it is generally true (to the extentthat the two configurations are indeed very similar) that the forwardoperator can be approximated by the pre-computed one. Therefore,benefits can be drawn both as far as efficiency and efficacy ofregularization. For example, applying the pre-computed inverse to theobserved electrode potentials will yield an excellent initial guess foriterative linear system solvers, significantly reducing the computationtime. Further, an SVD-based low-rank approximation of the pre-computedoperator A is likely to be a good preconditioner for the same iterativesolvers further reducing the computation time. Finally, representing toendocardial potential in the right singular functions of thepre-computed operator A can be a very effective form of regularizationand further diminish the required computation time by dramaticallyreducing the number of degrees of freedom from about 3000 to typicallyunder 100.

In some embodiments, pre-computed matrices generated for selectlocations of the catheter may be used to estimate the matrices for otherlocations of the catheter 110 with respect to which correspondingforward matrices were not pre-computed. Perturbation analysis can beused to do this. For example, suppose that a satisfactory inverseoperator has been constructed for a certain relative configuration ofthe endocardium and the catheter. Subsequently, the electrophysiologistmoves the catheter by a small amount. A boundary perturbation techniquecan be used for reconstructing the potential on the endocardium byutilizing the measurements on the displaced catheter, the known amountof displacement, and the inverse operator constructed for the originalposition. For example, the matrix for the new location can be determineby using the pre-computed matrices for nearby locations, or componentstherein, as bases in a perturbative solution (e.g., a linearsuperposition of the basis functions or matrices) for determining thematrix for the new location.

In some embodiments, pre-computed transformation matrices may begenerated for separate representations of the endocardium surface incircumstances where different representations for different phases ofthe heart's cycle are used. Thus, for each of those separaterepresentation of the endocardium surface, corresponding pre-computedmatrices are generated that are based on the individual geometry ofthese endocardium surface representations. Similarly, where severaltypes of catheters may be used (or where a catheter may have differentpossible catheter configurations), the matrices which depend on thegeometry of the catheter (e.g., −S_(c→c)) may be individually generatedfor each of those catheters. During the reconstruction procedure, thematrices corresponding respectively to the chosen catheter and to theparticular endocardium surface geometry are retrieved from storagedevice 160 and used to complete the computation of the full forwardmatrix corresponding to the particular location at which raw data wasacquired.

Catheter Registration

As explained above, an important aspect of the non-contact mappingprocedure is the determination of the catheter's position relative tothe endocardium surface representation. The relative position of thecatheter with respect to the endocardium surface representation isrequired, among other reasons, to compute the reconstructiontransformation functions to compute the physiological information at theendocardium surface.

FIG. 6 is a flowchart of an exemplary embodiment of a catheterregistration procedure 600 for determining a transformation functionthat aligns the catheter's coordinate system with the coordinate systemof the endocardium surface representation, and thus enables expressingthe position of the catheter's location in terms of the endocardiumsurface's coordinates system. The following will describe two methodsfor performing catheter registration. The first method relates to apoint cloud to surface registration while the second method employs theidentification of fiduciary anatomical markers.

FIG. 6 describes a point cloud to surface registration. As shown in FIG.6, a catheter is first inserted into the heart chamber and is moved to alocation where the catheter and/or at least one of its electrodes touchthe endocardium surface (at 610). In some embodiments an operator movesthe catheter 110 inside the heart chamber until it determines that thecatheter, or one or more of its electrodes, touches the endocardiumsurface. In determining whether the catheter 110 or any of itselectrodes are touching the walls of the endocardium surface, theoperator may be guided by visual aides such as a real-time ultrasoundsystem providing a visual image of the catheter inside the heartchamber, a camera coupled to a fibreoptic strand connected to thecatheter, fluoroscopy, impedance measurements, the size and shape ofintracardiac electrograms, pressure sensors fitted on the tip of thecatheter etc. Additionally, the operator may determine that the catheteris touching the endocardium surface when it encounters the highermechanical resistance exerted by the endocardium walls, thereby alertingthe operator that the catheter is at the endocardium surface. In yetfurther embodiments, a catheter may be guided to the endocardium wallsautomatically with minimal intervention from the operator.

Once the catheter has been placed in a location abutting the endocardiumsurface, the 3D spatial coordinates of the catheter (and/or itselectrodes) is determined at 620. The spatial coordinates may beestablished using one of several conventional sensing and trackingsystems. Such conventional sensing and tracking systems (also referredto as localization systems) include systems that determine the locationof the tracked object (in this case the catheter and/or its electrodes)using magnetic fields, electric fields, fluoroscopy, and ultrasoundsignals. These systems localize the catheter in the 3D space of thelocalization system.

See, for example, any of U.S. Pat. No. 5,697,377 entitled “Cathetermapping system and method,” U.S. Pat. No. 5,983,126 entitled “Catheterlocation system and method,” U.S. Pat. No. 6,690,936 entitled “Systemfor determining the location and orientation of an invasive medicalinstrument,” U.S. Pat. No. 5,713,946 entitled “Apparatus and method forintrabody mapping,” U.S. Pat. No. 5,694,945 entitled “Apparatus andmethod for intrabody mapping,” U.S. Pat. No. 5,568,809 entitled“Apparatus and method for intrabody mapping,” U.S. Pat. No. 5,833,608entitled “Magnetic determination of position and orientation,” U.S. Pat.No. 5,752,513 entitled “Method and apparatus for determining position ofobject,” and U.S. Pat. No. 6,427,314 entitled “Magnetic determination ofposition and orientation,” and U.S. Patent Application Publicationentitled “Method and apparatus for catheter navigation and location andmapping in the heart.”

As noted above, in some embodiments, the location of the electrodesrelative to the catheter 110 is fixed and known, and thus the onlyinformation that needs to be determined is the location and orientationof the catheter 110 in the 3D space established by the localizationsystem. In other embodiments, the location of the various electrodesrelative to the catheter may vary, and accordingly in such embodimentselectrodes may be tracked individually relative to the endocardium, orrelative to a location on the catheter with known position relative tothe endocardium. Electrode tracking may employ any of the abovementioned tracking methods such that instead of tracking only one knownpoint on the catheter, each electrode is individually tracked.

At 630 it is determined if additional catheter locations are required toperform the registration procedure. A minimum of three (3) separatecatheter locations are required to obtain an accurate geometrictransformation between the catheter's coordinate system and theendocardium's surface representation's coordinate system. However, toimprove the accuracy and reliability of the coordinate systemtransformation, more catheter locations could be used.

If additional locations are required, the procedure depicted at 610-620is repeated. Particularly, the operator moves the catheter 110 to thenext point on the endocardium surface, and the 3D coordinates of thecatheter 110 relative to the localization system are determined.

Subsequently, after N locations of the catheter 110 at the endocardiumsurface have been acquired, and their 3D spatial coordinates relative tothe localization system determined, the registration transformation, t₀,is computed at 640. As described above, to map the localization system'scoordinate system to the endocardium surface representation's coordinatesystem, the computed geometric transformation is the one that bestmatches the 3D locations of the catheter 110, as determined at 610-630,to the endocardium surface representation.

In some embodiments, computation of the registration transformation t₀is performed by minimizing the following expression:

$\begin{matrix}{\min\limits_{t_{0}}{\sum\limits_{i = 1}^{N}\; d_{i}^{2}}} & (17)\end{matrix}$

To perform the minimization of Equation (17), the surface S,representing the segmented boundary of the endocardium surface andnearby vessels, is defined. Also defined are the vector p_(i), whichcorresponds to the 3D spatial coordinates measured for the rovingcatheter using the localization system, and the operator T[t₀](p_(i))which is the transformation operator performed on the points p_(i). Theresultant vector t₀ is represented as a six parameter transformation[x₀, y₀, z₀, θ₀, φ₀, ψ₀] that is applied to catheter locations toexpress those locations in terms of the endocardium surface coordinatesystem.

The distance function D is defined such that d_(i)=D(T[t₀](p_(i)), S)represents the distance from transformed point T[t₀](p_(i)) to thesurface S. To determine the vector t₀ with respect to which the termd_(i) for the acquired N catheter locations on the endocardium surfaceis minimized, a number of techniques may be used, including conventionaliterative optimization techniques such as least-square error computationprocedures and/or other mathematical regression and curve-fittingtechniques.

In other embodiments, determination of the transformation vector t₀ maybe achieved using the fiduciary anatomical markers technique. In thistechnique, a number of fiduciary markers are identified in both theendocardial boundary and the localization system respective coordinatesystems. Once identified, the transformation that yields the minimummean square error between the two point sets is chosen.

For example, anatomical landmarks that are easy to identify in bothmodalities (Mitral annulus, coronary sinus, etc.) are used as thefiduciary markers. These landmarks may be easily identified in thepre-acquired endocardial boundary. Once a given landmark is identified,the catheter may be advanced to the landmark guided, for example, byfluoroscopy. Once in contact, a reading from the catheter localizationsystem is taken to establish the catheter's position in terms oflocalization system's coordinate system.

A variation of the above technique may be to place surface markers onthe patient that are easy to identify in both the pre-acquired andreal-time localization modalities.

The registration process yields a transformation vector that includesthree (3) displacement parameters (x₀, y₀, z₀) and three rotationparameters (θ₀, φ₀, ψ₀). This transformation can then be applied to 3Dspatial coordinates obtained by the localization system to obtain theroving catheter's location in terms of the endocardium surfacerepresentation's coordinate system. The mapped locations of thecatheter's 110 are subsequently used to compute the reconstructiontransformation, and/or perform all other computations that require thecatheter's location in terms of the endocardium surface's coordinatesystem.

It is to be noted that for a healthy patient in sinus rhythm theendocardial boundary is relatively fixed throughout the propagation ofthe activation wavefront. This implies that a small error tolerance isavailable when basing the registration process on a single boundaryshape for the construction of the geometrical mapping between thelocalization system and endocardial surface representation respectivecoordinate systems.

In some cases changes in blood volume between the time of imageacquisition and the time of registration or the presence of a persistentarrhythmia may lead to a change in chamber volume, and therefore amismatch between the preacquired and current endocardial surface. Suchmismatch leads to error when performing reconstruction of physiologicalsignal on the endocardial surface. Volume changes are generally lowerthan 20%. A number of methods may be used to compensate for this volumechange. One method is to add a scaling parameter that uniformly dilatesor contracts the endocardial representation relative to the acquiredpoint cloud. When performing the abovementioned minimization of equation(17) a scaling parameter s₀ may be added to the transformation vector.Rather than optimizing for 6 parameters, the minimization algorithmoptimizes for 7 parameters providing the scaling factor which isexpected to be in the range of ±20%. Other, more elaborate methods, mayalso scale the endocardial surface non-uniformly such that anatomicalareas that are a-priory known to be less likely to experience a changein shape due to volume changes are scaled less than those more likely tochange.

In case of persistent arrhythmia the heart may experience mechanicalchange during the activation wavefront propagation. As discussedpreviously, it is possible to obtain endocardium boundary representationfor multiple phases of the mechanical cycle. Thus, in some embodimentsseveral geometrical transformation vectors, such as t₀, corresponding tomultiple heart shapes may be computed. The system may detectphysiological data such as ECG, intracardiac electrograms and strokevolume using impedance plethysmography and use this data to select thecardiac phase, appropriate endocardial boundary representation and thecorresponding geometric transformation t₀.

It will be appreciated that while the transformation t₀ establishes thegeometric mapping with a respect to a representative point on thecatheter 110 (e.g., the tip of the electrode that touched theendocardium surface when the catheter was moved around, the centralpoint on the body of the catheter, etc.), the coordinates of any pointon the catheter and/or its electrode in terms of the endocardium surfacerepresentation's coordinate system can be determined.

Reconstruction of Physiological Information at the Endocardium Surface

Given the relative location of the catheter and/or its electrodes to theendocardial boundary, the numerical transformation from the signalsmeasured by the electrodes to the physiological information (e.g.,electrical potentials) at the endocardial surface can be computed.

The physical laws governing the reconstruction of the physiologicalinformation at the endocardium surface are briefly summarized below:

The potential V in a homogeneous volume Ω is governed by Laplace'sequation∇² V=0  (18)

-   -   subject to boundary conditions

$\begin{matrix}{{{V = V_{e}},{{on}\mspace{14mu}{the}\mspace{14mu}{surface}\mspace{14mu} S_{e}}}{{\frac{\partial V}{\partial n} = 0},{{on}\mspace{14mu}{the}\mspace{14mu}{surface}\mspace{14mu}{S_{c}.}}}} & (19)\end{matrix}$where S_(c) is catheter surface and the vanishing normal derivativeaccounts for the fact that the current does not penetrate S_(c). S_(e)represents the endocardial surface. In case of a branch-shaped catheters(or otherwise porous, hollow catheters), where the extent of blooddisplacement in the heart chamber is much less significant than forballoon-shaped catheters, the constraint of vanishing normal derivativemay be omitted.

As previously alluded to, with the exception of a handful of geometries,Laplace's equation needs to be solved numerically. Numerical methodssuch as boundary element method (BEM), finite element method (FEM),finite volume method, etc. may be used to solve Laplace's equation. Forsome special geometries, such as near-spherical geometries, sphericalharmonics may be used. Each numerical method represents the geometry ina discrete way, but each method uses its own representation. In allnumerical methods the potentials on the endocardial surface and on thecatheter are represented by finite-dimensional vectors. Since Laplace'sequations are linear, these vectors are related by a matrix A, known asthe forward matrix:V _(e) =AV _(e)  (20)where V_(c) is a vector containing the potentials measured by theelectrodes on the catheter and V_(e) is a vector containing the realendocardial potentials. The matrix A has dimensions of m×n, where m isthe number of electrodes on the catheter and n is the number of degreesof freedom in the endocardial potential, usually the number of surfaceelements used to represent the surface S_(e). Typically m<n. However, asnoted, by moving the catheter around the heart chamber and subsequentlycomputing a reconstruction transformation that is applied to a compositeof the raw data corresponding to the signal acquired at the multiplelocations, the effective number of electrodes m can be increased,thereby reducing the disparity between m and n and thus improving theaccuracy of the computations.

Equation (20) provides a transformation relationship from theendocardial to the catheter potentials. This relationship, which isgenerally referred to as the forward problem, is well posed and can besolved with great precision. To provide the transformation from catheterto endocardial potentials, {circumflex over (V)}_(e), a vectorrepresenting the estimated endocardial potentials, has to be determinedgiven V_(c). Since matrix A is generally rectangular, underdetermined,and rank deficient to within IEEE double precision, solving thisequation is difficult, if not impossible.

The first step towards calculating {circumflex over (V)}_(e) is toreformulate Equation (20) as a least squares problem in which theexpression∥V _(c) −A×{circumflex over (V)} _(e)∥²  (21)also referred to as the objective function is minimized over allpossible {circumflex over (V)}_(e). The matrix A is either determined inreal-time, or, as described above, a pre-computed matrix A correspondingto the particular geometry of the catheter 110 (including the positionand orientation of the catheter) is retrieved from storage device 160.As also described above, if the storage device does not storefull-forward transformation matrices A corresponding, for example, tothe particular position(s) and/or orientation(s) of the catheter 110,the computation of the matrix A can nevertheless be expedited byretrieving from storage device 160 partially pre-computed functions orfunction components, and completing the computation of the matrix A asdescribed with reference to Equation (16).

If A were over-determined (i.e., its rank exceeding the dimension ofV_(c)) and typically it is not, then {circumflex over (V)}_(e) could intheory be determined by the classical least squares formula:{circumflex over (V)} _(e)=(A ^(T) A)⁻¹ AV _(c)  (22)

However, because A is generally undetermined and, as a result, A^(T)A issingular and cannot be inverted, the expression in Equation (22) cannotusually be applied in practice.

One difficulty relating to performing a least-square error procedure isthat because the matrix A attenuates the physiological signal, theinverse operation needs to amplify the signal. The level of attenuationin the forward and amplification in the inverse depends on the size andthe location of the catheter and the nature of the electrical potentialon the endocardium.

In practice, significant components of V_(e) are attenuated on the orderof 1000 or greater. As a result, small errors in V_(c) will producelarge errors in {circumflex over (V)}_(e). Thus the inverse problem isill-posed.

A regularization technique can be used for dealing with the ill-posednature of the inverse problem. The regularization technique involvesmaking additional assumptions about the behavior of the endocardialsignal. These assumptions may relate to the spatial or temporalcharacteristics of the physiological information at the endocardiumsurface.

One technique that can be used is the zeroth (0^(th)) order Tikhonovregularization technique. The technique is predicated on the assumptionthat the essential part of the signal is contained among the rightsingular values of A that correspond to the lowest singular values. Analternative geometric interpretation is that Tikhonov regularizationlimits the amplitude of spatial variation of the reconstructed signal.The zeroth order Tikhonov regularization results when a term thatpenalizes large endocardial potentials is added to the objectivefunction. Thus, the least square error problem defined in Equation (21)can be re-formulated as:∥V _(c) −A×{circumflex over (V)} _(e)∥² +t∥{circumflex over (V)}_(e)∥²  (23)

Where a minimization is performed over all possible {circumflex over(V)}_(e). Solving this minimization yields the expression{circumflex over (V)} _(e)=(A ^(T) A+tI)⁻¹ AV _(e)

The regularization parameter t controls the amount of spatial smoothingapplied to reconstructed potentials {circumflex over (V)}_(e). Theregularization parameter t provides a trade off between spatialresolution and sensitivity to noise. As t decreases, the reconstructionresolution is improved, but noise and the instability of the solutionincreases. In some embodiments, t may be chosen such that it is three(3) times the root mean square value of noise detected by theelectrodes. Other methods such L-curve may be used to find an optimalregularization parameter.

In addition to addressing the ill-posedness of the inversion problem,Tikhonov regularization also solves the problem of theunder-determination of A.

FIG. 7 is a flowchart of an exemplary procedure 700 for reconstructingphysiological information from signals acquired by the multipleelectrodes of the catheter 110.

As shown, the catheter 110 is moved to one of multiple locations withinthe heart chamber at 710. In some embodiments an operator controls themovement of the catheter and decides its next location, while in otherembodiments the catheter's movement is fully or partially automated.

Once the catheter has reached a location in the heart chamber, theposition of the catheter 110 in relation to the endocardium surfacerepresentation is determined at 720. Particularly, the localizationsystem tracking the location of the catheter 110 determines the 3Dspatial coordinates of the catheter 110 relative to the localizationsystem. The localization system thus provides the position andorientation of the catheter 110 in terms of the localization systemcoordinate system. The previously determined geometric coordinatetransformation vector t₀ is then applied to the position of the catheter110, as expressed in terms of the localization system's coordinatesystem, and transforms that position to a resultant catheter positionexpressed in term of the endocardium surface's coordinate system.

The catheter's multiple electrodes then acquire the raw data signalsthat resulted from the electrical activity of the heart and send thesignals to the processing unit 120 at 730. In some embodiments theacquired signals are electrical and/or magnetic signals resulting fromthe electrical activity of the heart. As will become apparent below, toreduce the error associated with the measurement of the signals, in someembodiments, the catheter's multiple electrodes acquire multiple sets ofsignal in each heart beat over several heart beats.

Additionally, in some embodiments signal acquisition is performed inseveral locations in the heart chamber. Under these circumstances themultiple sets of signals are processed (e.g., by performing an averagingor a weighted averaging operation) to generate a resultant set of rawdata on which the reconstruction procedure will subsequently beperformed. A forward transform A is then constructed for the compositeraw data set that includes data from multiple catheter locations, andthe reconstruction set of physiological data, corresponding to thecomposite set, is then determined. To consolidate the signals from thecatheter's various locations into a composite set, a synchronizationmechanism may be used to enable the system 100 to acquire signals atsubstantially the same cycle of heart's electrical activity. Thesynchronization could be based on physiological data (e.g., ECGmeasurements, intracardiac electrogram measurement, operator pacing)collected by the synchronization mechanism. Accordingly, thereconstruction of physiological information from a composite setobtained in the above-described manner results in processing thesynchronized raw data signals as though they were obtained at one timefrom all the positions sampled by the catheter's electrodes for thedifferent positions of the catheter in the heart chamber.

Having acquired the raw data, the forward reconstruction transform A isdetermined at 740. As explained above, the forward transform A dependson the position and/or orientation of the catheter 110 relative to theendocardium surface representation. In some embodiments, determining thematrix A includes computing the values of the forward matrix inaccordance with the expression (−S_(e→c)+I+O_(e→c)O_(e→e)⁻¹S_(c→e))⁻¹(O_(e→c)O_(e→e) ⁻¹(I−S_(e→e))+S_(e→c)), as more particularlyexplained above. In those embodiments where an actual computation of theabove expression is carried out in real-time or near real-time, thecomputation is expedited by retrieving from storage device 160 (or someother memory device) pre-computed reconstruction matrix components suchas the components relating to S_(e→e), O_(e→c), and O_(e=e) ⁻¹. Underthese circumstances, the task of computing the forward transform A for aparticular set of raw data is reduced to completing the computation ofan already existing partially computed forward transform A by generatingthe missing components based on, for example, the particular positionand orientation of the catheter 110.

In other embodiments, fully computed forward matrices, eachcorresponding to a particular position of the catheter, can be retrievedfrom storage device 160. In those embodiments the position of thecatheter 110 and/or its electrodes is used to access a look-up tablethat maintains the various pre-computed forward transforms.

Having determined the forward transform, the reconstructed set ofphysiological information (e.g., electrical potentials) at theendocardium surface representation is determined at 750. In particular,a regularized inversion procedure as described above is used to estimatethe values of the reconstructed physiological information set based onthe set of raw data that was acquired (be it a set of data acquired froma single measurement by the multiple electrodes or some resultant setderived from multiple measurements) and the forward transform A that wasdetermined at 750 (for example, as described above with respect toEquation (21)).

Once the reconstructed set of physiological information has beencomputed, the physiological information can be overlaid on theendocardium surface representation using, for example, conventionalgraphic display techniques (e.g., graphical rendering). Post-processingoperations may be additionally applied to the set of reconstructedphysiological information. The physiological information can bedisplayed, for example, using a color code whereby a color is assignedto ranges of values. A certain value corresponding to a particularsurface element is mapped to a corresponding color which is then used tofill the area on the graphical representation of the endocardiumassociated with the surface element with that color. Other ways torepresent the values of the physiological information can be used.

Signal Acquisition Over Multiple Heart Beats

As described herein, the reconstruction of physiological information atthe endocardium surface is affected by noise. To control the effectnoise has on the reconstructed information at the endocardium surfacerepresentation, the Tikhonov regularization technique can be used,whereby the regularization parameter t is chosen, in some embodiments,to be three times the root mean square of noise. Although the Tikhonovregularization technique helps to reduce the computation error due tonoise, the technique adversely affects the spatial resolution of thereconstructed information. The larger the regularization parameter, themore spatially smooth (and hence low resolution) the reconstructionbecomes. It is therefore preferable to reduce all sources of error whenperforming the reconstruction. The signals detected by catheterelectrodes should be shielded to reduce interference and conditioned bya low noise input stage.

In addition to these measures, it is also possible to improve signal tonoise by conducting measurements over a period of multiple heart beats.The signal to noise ratio can be improved by a factor of √{square rootover (B)} where B is the number of measurements in the presence of aperiodic signal and independent and identically distributed noise. Thus,in situations involving periodic arrhythmias, under the assumption thatmultiple beats are identical, it is possible to improve signal-to-noiseratio by sampling over multiple beats.

In situations where the catheter 110 maintains a fixed location, theforward transformation A remains constant between beats. The vectorV_(c)(t) is defined as the measurements made on the catheter at instantt. If the signals are sampled at increments of Δt (e.g. 1 mS), then fora heart beat having a period P (e.g. 750 mS for 80 beats per minute) themeasured signals can be expressed as V_(c)(s·Δt+P_(b)) or V_(c)(s,b)where s is the phase number in the cycle, and P_(b) is the time stamp atwhich the fiducial marker associated with beat b was detected. Theparameter s can thus be regarded as a specific phase in the cardiaccycle. To assign a measurement at time t with the appropriate values ofs and b, a specific fiducial marker needs to be identified within eachheart beat. This can be done by relying on reference signals such asbody surface ECG or intracardiac signals such as those collected in thecoronary sinus. A specific implementation of this is described furtherbelow in the next section.

If data over a number of beats equal to B is collected, then Bmeasurements of signals are available for the phase s of the heartcycle. Thus, the B measurements at the same phase can be averagedaccording to:

$\begin{matrix}{{{\overset{\_}{V}}_{c}(s)} = {\frac{1}{B}{\sum\limits_{b = 1}^{B}\;{V_{c}\left( {s,b} \right)}}}} & (24)\end{matrix}$

Since the resultant set of signals corresponds to data averaged over Bmeasurements, assuming independently and identically distributed noisesources an improvement in signal to noise proportional to √{square rootover (B)} is obtained. The resultant averaged set of signal values arenow used to perform the reconstruction of the physiological information,and accordingly the reconstruction resolution can be increased by usingthe averaged data and reducing the value of the Tikhonov regularizationparameter t by a factor of √{square root over (B)}.

Additional improvement in reconstruction accuracy can be obtained bymoving the catheter. Since catheter movement is slow relative to theheart rate, when moving the catheter raw signals are acquired atmultiple locations over multiple beats. Catheter movement and the use ofmultiple beats have several advantages. One advantage is that use ofmultiple beats improves the signal-to-noise ratio. Another advantage isthat the movement of the catheter allows improved resolution in areasthat the catheter was moved closer to, and effectively provides signalmeasurements from more electrode locations (thereby effectivelyproviding more electrodes).

Unlike the fixed position catheter scenario, however, when the catheteracquires its measurements at multiple locations, the forward transformmatrix A does not remain constant.

Generally, each catheter location where raw data is acquired would beassociated with a corresponding forward transformation A_(b). Aftercollecting data over multiple beats in multiple locations and detectingthe phase s for each measurement, a new measurement vector V_(c) can beassembled that contains all electrode measurements conducted at the same(or substantially the same) phase s. Thus, the vector V_(c)(s) can beexpressed as:

$\begin{matrix}{{V_{c}(s)} = {\begin{matrix}{V_{c}\left( {s,1} \right)} \\{V_{c}\left( {s,2} \right)} \\\vdots \\{V_{c}\left( {s,B} \right)}\end{matrix}.}} & (25)\end{matrix}$

Additionally, a composite forward transform Ã is defined such that:

$\begin{matrix}{\overset{\sim}{A} = \begin{matrix}A_{1} \\A_{2} \\\vdots \\A_{B}\end{matrix}} & (26)\end{matrix}$where A_(b) are determined as described above.

The relationship between the composite vector V_(c)(s), the compositeforward transform Ã and the reconstructed set of physiologicalinformation (in this case, electrical potentials) is expressed as:Ã·V _(e)(s)=V _(e)(s)  (27)

Using the relationship articulated in Equation (27), the values ofV_(e)(s) (i.e., the reconstructed set of physiological informationcorresponding to a particular phase s) can be determined by performingthe inverse procedure previously discussed. For example, a regularizedinversion of Ã may take place as discussed above.

The effect of moving the catheter for a periodic arrhythmia is similarto the effect of having multiple catheters in a single beat.

Until now it was assumed that the signal propagation is periodic, andthat therefore V_(e)(s,b₁)=V_(e)(s,b₂) for any b₁ and b₂. However, ifthe cardiac propagation is non-periodic, this assumption is notnecessarily valid, and therefore an averaging operation to improve, forexample, the signal-to-noise ratio, may not be feasible.

Nonetheless, even in situations involving non-periodic signals, thereare several properties that remain substantially the same over multiplebeats. For example, tissue properties remain relatively unchanged over aperiod of several minutes. One way to characterizing tissue is throughthe formation of a voltage map (as will be described below). Under theassumption that the maximum voltage amplitude in a particular arearemains substantially constant over a period of multiple beats, amulti-beat enhancement of the voltage map resolution can be achieved.Particularly, the voltage map value can be defined as:

$\begin{matrix}{{VMV} = {{\max\limits_{s}\left( {{\hat{V}}_{e}(s)} \right)} - {\min\limits_{s}\left( {{\hat{V}}_{e}(s)} \right)}}} & (28)\end{matrix}$

As data is collected over B beats and a reconstruction operation isperformed for each beat, B voltage maps VMV_(b) are produced.

Let us suppose that we would like to find the VMV for a particular pointon the endocardium for which we know both the voltage map values foreach beat (VMV_(b)) and the reconstruction resolution for eachmeasurement Res_(b) (derived in a manner described below). We may nowassign a new weighted average value for this VMV where

$\begin{matrix}{{\overset{\_}{VMV} = {\frac{1}{\sum\limits_{b = 1}^{B}\;\beta_{b}} \cdot {\sum\limits_{b = 1}^{B}\;{\beta_{b} \cdot {VMV}_{b}}}}}{{{where}\mspace{14mu}\beta_{b}} = {\frac{1}{{Res}_{b}}.}}} & (29)\end{matrix}$

It will be appreciated that other types of averaging schemes todetermine the average VMV may be utilized.

Signal Phase Alignment

In order to process data acquired over multiple beats it is necessary toalign the data relative to a specific phase in the electrical cycle. Thefollowing describes a method of aligning the K signals V₁(t)−V_(K)(t),as shown in FIG. 12.

In the first stage, all signals are amplified, filtered and sampled. Asynchronization signal is concurrently acquired in an identical manner.The synchronization signal can be acquired from surface ECG, or anintracardiac signal in a fixed location such as that detected by acoronary sinus catheter.

The Fiducial Point Detector (FPD) detects the time markers at whichparticular event occur. For example, the FPD may detect the R wave insurface ECG or activation time of an intracardiac electrogram. Thedetection is performed in a manner similar to alignment methods foraveraging of high resolution ECG. See, for example, Jane Raimon,“Alignment methods for averaging of high resolution cardiac signals”,IEEE Transactions in Biomedical Engineering, Vol. 38 No. 6 (June 1991);Brooks, Dana, “Improved alignment method for noisy high-resolution ECGand Holter records using multiscale cross-correlation”, IEEETransactions in Biomedical Engineering, Vol. 50, No. 3 (March 2003);Breithardt, Gunter, “Standards for analysis of ventricular latepotentials using high-resolution or signal-averagedelectrocardiography”, Circulation, Vol. 83, No 4 (April 1991).

Briefly, a template signal is cross-correlated with the synchronizationsignal. Fiducial points are detected when the cross-correlation betweenthe template and synchronization signal reach a maximum. The templatesignal itself may be a relatively clean signal that was acquired fromthe synchronization signal previously, or an average of a number ofthese signals. The template signal may be selected visually by the useror automatically by a computer algorithm which uses a priori knowledgeabout the statistics of the signal.

It should be noted that in case of mapping performed during pacing, thesynchronization signal may come from the pacing apparatus. In this case,no cross-correlation is necessary and the FPM will just pass the timemarkers associated with the synchronization signal.

The FDM outputs the time markers P₁ . . . P_(B) at which the fiducialpoints were detected. These time markers are then used to align theacquired signals. After the acquired signals are aligned, they areexpressed as V_(K)(s,b), where s is the phase number in a particularbeat b.

Post Processing and Visualization

A number of post-processing and visualization techniques may be used todisplay the reconstructed physiological information in a clinicallymeaningful manner. Some of the post-processing operations include thefollowing.

A. Resolution Display

As discussed previously, the potential is greatly attenuated on its wayfrom the endocardial surface to catheter electrodes. The potential maybe attenuated so much, that by the time it reaches the catheter itsvalues are lower than the noise floor. Due to this attenuation,potentials (or other types of physiological information) on theendocardium surface representation in areas that are closer to thecatheter may be reconstructed with greater accuracy and spatialresolution than potentials in areas farther away from the catheter. Toincrease the utility of reconstructed potentials such that clinicaldecisions can be aided by the information they provide, it is useful toprovide the physician with information about the fidelity of thereconstruction.

In some embodiments a heuristic approach that is designed to measure howmuch of the signal remains above noise by the time it reaches to thecatheter is used to compute resolution maps. Generally, signalsexperience attenuation which depends, among other things, on the spatialfrequency of signal. That is, the higher the spatial frequency, thegreater the level of attenuation.

The singular value decomposition of the forward transform A suggests anapproach for determining the attenuation levels of the forwardtransformation. Particularly, in the singular value decomposition of A,the right and left singular vectors R_(i) and L_(i) are uniqueorthonormal bases that map one to another under A and are relatedthrough the vector σ_(i) which represents an attenuation of one basis,relative to the other. Thus, decomposing the endocardial signal V_(e)under A with respect to the right singular vectors of A provides:

$\begin{matrix}{V_{e} = {\sum\limits_{i}\;{\alpha_{i}R_{i}}}} & (30)\end{matrix}$

Accordingly:

$\begin{matrix}{V_{c} = {{AV}_{e} = {\sum\limits_{i}\;{\alpha_{i}\sigma_{i}L_{i}}}}} & (31)\end{matrix}$

It follows that the portion of the signal proportional to the singularvector R_(i) is “reconstructible” if α_(i)σ_(i) exceeds the level ofnoise. In some embodiments, an endocardial signal V_(e) is deemed to bereconstructible if the singular value components that remain above noiseafter being transformed by A add up to at least 60% of the total signalenergy. The resolution at a particular point i on the endocardium issaid to be Res_(i) if a bell-shaped signal centered at that point andhaving a “standard deviation” of Res_(i) is reconstructible.

Another approach to determine the resolution of the reconstructedphysiological information at the endocardium surface representation alsorelies on the use of the forward transform A. As was explained, A is amatrix of size m×n where the number of rows m corresponds to the numberof catheter electrodes and the number of columns n is the number ofelements on the mesh describing the shape of the endocardium surface.For example, the values in column i in A can represent the voltagemeasurements on all electrodes had there been a potential of 1V on thei^(th) element on the mesh and a potential of 0V everywhere else.Therefore, summing the squared values in a particular column i anddividing the sum by the number of electrodes, yields a valueproportional to the average amount of energy that propagated fromelement i on the heart surface to the catheter's electrodes. Thus, theresolution Res_(i) for the i^(th) element on the endocardium surface canbe expressed as:

$\begin{matrix}{{Res}_{i} = {\frac{1}{m}{\sum\limits_{j = 1}^{m}\; A_{j,i}^{2}}}} & (32)\end{matrix}$

It will be appreciated that each squared entry in the column correspondsto the energy received by one of the catheter electrodes (j in the aboveequation represents the j^(th) electrode of the catheter).

The average amount of energy that makes it from a particular element ion the heart's surface to the catheter's electrodes is a good indicatorof the reconstruction resolution. Areas on the heart surface whosevoltage is attenuated greatly by the time they reach the catheter arereconstructed with poor resolution while areas whose voltage arrivesless attenuated are reconstructed more accurately.

In general, reconstruction resolution on a particular point on theendocardial boundary depends on that point's distance to the catheterand the solid angle at which the catheter appears from that point.

FIG. 8 is a flowchart of an exemplary embodiment of a procedure 800 togenerate a resolution map. As shown, a forward transform A,corresponding to a particular catheter location is first obtained at 810using, for example, either of the techniques described above.

It will be appreciated that other techniques for determining aresolution map having values indicative of the degree of spatialresolution of the determined physiological information for at least somelocations at the endocardium surface can be used.

Once generated, the resolution map may be displayed at 830 to thephysician to aid in determining the accuracy and reliability of thereconstructed physiological information. In preferred embodiments theresolution map is overlaid (i.e., superimposed) with the reconstructedphysiological signal on the endocardial representation by one of thefollowing techniques:

-   -   1. Grid Lines—The resolution values can be represented on the        endocardial representation by controlling the grid line density        appearing on the endocardial representation. Thus, in areas of        high resolution the grid lines on the endocardial representation        will appear dense while in areas of low resolution they will        appear coarse. Similarly, the resolution can be displayed by        superimposing dots on the endocardial representation. In this        case resolution is displayed by controlling dot density, instead        of grid line density, such that dot density in high resolution        areas will be higher than in areas of low resolution.    -   2. Transparency—The resolution values can also be represented on        the endocardial surface by controlling the transparency of the        displayed areas. Thus, areas of high resolution will appear        opaque while areas of low resolution will appear increasingly        transparent.    -   3. Brightness—The resolution values can also be represented by        controlling the brightness of the displayed areas on the        endocardium surface representation. Thus, areas of high        resolution will appear bright while those of low resolution will        appear increasingly dark.

In some embodiments instead of assigning overlaying the resolution valuea thresholding scheme may be used. A minimum resolution threshold (forexample 1 cm) may be defined. Areas where the resolution is better thanthe threshold will be displayed (e.g. opaque, bright, etc.) withcorresponding physiological information while areas of low resolutionwill be masked (e.g. transparent, dark, etc.).

In yet other embodiments the resolution may be displayed on anindependent endocardial representation, alongside an additionalendocardial representation depicting the physiological information.

The physician may use this resolution map to determine the reliabilitywith which data is reconstructed on the endocardium surfacerepresentation. If an inadequate resolution is available at a point ofinterest, the physician may advance the catheter towards the point ofinterest to improve reconstruction resolution.

Additionally, in some embodiments generated resolution maps are used toconstruct a composite set of reconstructed physiological information. Inparticular, in circumstances where for each location of the catheter aseparate reconstructed set of physiological information (which may havebeen obtained over multiple heart beats and/or multiple locations) isavailable, a corresponding resolution map, for each of those availablereconstructed sets, is generated using, for example, one of thetechniques described above. Subsequently, a resultant composite set ofreconstructed physiological information is generated by selecting, for aparticular surface element at the endocardium surface representation, areconstructed value from that set of reconstruction physiologicalinformation whose corresponding resolution map value is the best or mostoptimal or by performing a weighted average as described above.

B. Isopotential Representation

The reconstructed potentials provide a snapshot of potentialdistribution on the endocardium surface at a given instant. Suchpotential values may be color coded and superimposed on the endocardialrepresentation for display. For clarity, contoured isopotential linesmay also be added showing lines of interpolated equal potential.

The propagation of potentials (i.e., their temporal behavior) may alsobe calculated using multiple time instances and displayed in a similarmanner. As a result color data and/or isopotential lines will bedisplayed as an animation depicting the temporal behavior of potentialdistribution.

C. Timing Map

Timing maps display information pertaining to the timing of particularevents relative to the occurrence of other easily detectable referenceevents. This information may include temporal features such as the onsetof depolarization (activation), repolarization and activation duration.Reference events may include the R wave on an ECG or activation time ina specific intracardiac electrogram (e.g. electrode at coronary sinus).One type of timing map, activation time map (isochrone), is commonlyused to describe activation wavefront propagation. In this type of mapthe activation time of each point on the endocardium is determined andits value color coded and displayed on the endocardial surface. In otherwords, an isochrones map identifies the time instances at whichparticular locations on the endocardium surface experienced adepolarization of their potentials. The electrical activity during aheart beat cycle (or more) can thus be displayed on a single isochronescontour map showing lines of interpolated equal activation times. Theconstruction of the isochrones map requires the detection of activationinstants from reconstructed potentials.

FIG. 10 is a flowchart of an exemplary embodiment of a procedure 1000 togenerate an activation time map. As shown, multiple reconstructed setsof potential values relating to the endocardium surface are provided at1010.

Next, for each surface element associated with the series ofreconstructed sets, the activation time (i.e., the time at which thepotential was depolarized) at that surface element is determined at 1020based on the values of reconstructed sets. In some embodiments, theactivation time is determined by identifying the reconstructed set atwhich the rate of potential change was the highest. In some otherembodiments, the activation time is determined by identifying the firstreconstructed set at which there was a potential change, as compared tothe preceding reconstructed set, exceeding some pre-determinedthreshold. In yet other embodiments, activation time is determined usingcross-correlation with a template beat in the manner described above.Other ways for establishing the activation time instance can be used.The identified reconstructed set is associated with a particular timeinstance, which is recorded in the activation time map. It will beappreciated that the entries of the activation time map may haveinitially been set to a value that is indicative of no associatedactivation time (e.g., a value of 0 or a negative value).

Once the activation time (if any) has been determined for all thesurface elements of the endocardium surface representation, the derivedactivation time map is displayed at 1030.

While isochrones maps are helpful in depicting activation propagation ina single image, they can be limited in that they discard informationrelated to potential waveforms, amplitude and areas where multipleactivations per beat are present.

Additional processing may be conducted to highlight properties of theactivation propagation. For example, areas that have not been activatedor that experienced more than one activation in a given heart beat maybe highlighted for further investigation.

It will be appreciated that activation time maps may be generated forother types of physiological information.

D. Voltage Map

Voltage maps can be used to display characteristics of voltage amplitudein given areas of the endocardium surface. The voltage maps arecalculated from the reconstructed potentials over a single or multiplebeats. Useful information may be maximum amplitude, or root mean squarepotential value. Voltage maps are particularly useful for the detectionof infracted areas which tend to have lower amplitudes generally <1 mV.

FIG. 11 is a flowchart of an embodiment of an exemplary embodiment of aprocedure 1100 to generate a voltage map. As shown, one or morereconstructed sets of electrical potentials are provided at 1110. Thereconstructed set(s) may correspond to reconstructed potentials computedfrom a single measurement performed by the multiple electrodes of thecatheter 110, or alternatively may correspond to several measurementstaken over several heart beats, or may more generally correspond tomultiple measurements taken at multiple locations in the heart chamberover several heart beats and/or at different phases of the heart cycle.

The reconstructed sets provided are then processed to determine, foreach surface element of the endocardium surface representation, a metricvalue that is representative of the corresponding values from thereconstructed sets (at 1120). In some embodiments, the metric value isthe maximum amplitude potential value identified from the respectivevalues (provided by the available reconstructed sets) associated with asurface element. In some embodiments, the metric value is computed asthe root mean square of the various respective values from thereconstructed sets. Other representative values of the potential atvarious locations of the endocardium surface may be computed. Once themetric value for a particular surface element of the endocardium surfacerepresentation has been computed, that value is recorded in thecorresponding entry of the voltage map.

Voltage maps may have a large dynamic range of values. While healthyareas of the heart tend to have potential values in the range of 5-60 mVat the endocardium surface, infracted areas tend to have maximumamplitudes lower than 1 mV. This wide dynamic range makes it difficultto visualize these voltages effectively. To enhance visualization, thecolor map which assigns colors to voltages may be adjusted. One commonlyused adjustment is to define a range of interest such that values thatlie outside the range are clamped to either minimum or maximum rangevalue. Values in the range are linearly matched to a color map.

Thus, in some embodiments the metric values of the voltage map areconverted to corresponding color map values (at 1130). The voltage mapcontaining the color values computed at 1130 is subsequently displayedat 1140 on the endocardium surface representation.

FIG. 5 is an illustration of an exemplary voltage map generated using alinear color map matching scheme. As shown, areas of differingelectrical activity in the endocardium surface are readily discernable.Another useful color conversion scheme for the voltage map procedure1100 is a logarithmic color conversion scheme.

E. Difference Map

As previously described, another type of post-processing operation thatis based on voltage maps is the generation of a difference map. Thedifference map provides information regarding the effectiveness of theclinical procedure, such as an ablation procedure, performed on apatient to ameliorate the symptoms of arrhythmias. The difference mapcompares the electrical behavior of the heart, as reflected from two ormore voltage maps generated before and after the performance of theparticular clinical procedure.

Thus, after generating a first a voltage map, the clinical procedure,for example, an ablation procedure, is performed at the areas of theheart that are determined, aided by the information provided by thefirst voltage map, to require treatment. After the ablation procedurehad been performed, a second voltage map is generated. The values of thefirst ablation map are subtracted from the corresponding values of thesecond voltage map. If there is no significant difference between anythe respective voltage map entries corresponding to a particularendocardium surface locations where the ablation procedure wasperformed, this may be indicative that the ablation procedure performedat those locations had little clinical effect.

It will again be appreciated that maps analogous to the voltage mapsdescribed above may be generated for other types of physiologicalinformation. For example, the difference map could show differences inmeasured potential at a specific phase in the heart cycle.

F. Frequency Maps

As the understanding of fibrillation mechanisms develops, there is anincreased emphasis on using spectral analysis to guide treatment.Spectral analysis and frequency mapping are used to identify localizedsites of high-frequency activity during fibrillation. Ablation at thesesites results in changes and sometimes termination of the fibrillation,indicating their role in the maintenance of arrhythmia.

In spectral analysis frequency data is color coded and displayed on the3D anatomical endocardium surface representation. In some embodimentsthe data displayed is the dominant frequency at which activation takesplace in a given location.

For example, FIG. 9 is a diagram showing a time and frequencyrepresentations of an electrogram. As shown, the image on the leftdepicts the electrogram potential as a function of time in fibrillatingtissue. The image on the right depicts a Fast Fourier Transform (FFT) ofthe same signal. In this example, the dominant frequency (DF) of thesignal is 9.6 Hz. For each location of interest the DF can be calculatedby applying an FFT on the time-dependent signal, determining thefrequency at which maximum amplitude is present. The DF is then colorcoded and displayed on corresponding chamber anatomy.

Similar spectral analysis may thus be performed with respect to otherreconstructed sets of the endocardium surface. FIG. 13 is a flowchart ofan exemplary embodiment of a procedure 1300 for generating a frequencymap. As shown, multiple reconstructed sets of physiological informationat the endocardium surface are provided at 1310. The reconstructed setscorrespond to a temporal sequence of measurements performed by thecatheter 110. Subsequently, a frequency transform procedure, such as aFast Fourier Transform, is performed on the reconstructed sets at 1320.The frequency transform procedure is performed individually for thevalues of the reconstructed sets corresponding to individual surfaceelements of the endocardium surface representation. A representativevalue, for example a dominant frequency, obtained from the resultantfrequency representation of the time behavior for a particular surfaceelement is recorded in the frequency map entry corresponding to thatparticular surface element. In some embodiments, the temporal data withrespect to which the frequency transform is performed could correspondto multiple surface elements.

Once the frequency transform procedure has been completed, the frequencyrepresentation values are converted to corresponding color map values toenable the resultant values to be more easily observed when displayed(at 1330). The color frequency map, containing the color values computedat 1330, is subsequently displayed at 1340 on the endocardium surfacerepresentation.

Applications

Mapping of electro-anatomical characteristics of heart tissue can behelpful in guiding therapies for a number of diseases includingarrhythmia and heart failure.

For targeted therapy of arrhythmia, it is necessary to accuratelyidentify the source of the arrhythmia. The source of the arrhythmia maybe identified by electro-anatomically characterizing the underlyingtissue during sinus rhythm, spontaneous or induced arrhythmia, or duringpacing. Electro-anatomical characterization includes a number ofspatio-temporal features of the conduction. For example, these includeactivation time mapping to identify early activation sites that areindicative of exit sites and unwanted automatic cell firing, maximumvoltage to identify low voltage areas that are indicative of infractedregions, as well as repolarization time and spectral behavior. Once thesource of the arrhythmia has been identified, a therapeutic course ofaction is undertaken. Therapy may include ablation of tissueaccomplished by the delivery of RF energy, microwave energy, cooling,ultrasound, chemical agents, radiation or laser. Alternatively, therapymay also be accomplished by the introduction and targeted delivery ofbiological agents such as cells capable of performing myocardial repair,or genes capable of changing physiological behavior.

For cell therapy, see, for example: Ronglih Liao, Ph.D., “Cell TherapyAttenuates Deleterious Ventricular Remodeling and Improves CardiacPerformance After Myocardial Infarction”, Circulation (Apr. 10, 2001);Pieter C. Smits, “Catheter-Based Intramyocardial Injection of AutologousSkeletal Myoblasts as a Primary Treatment of Ischemic Heart Failure”,Journal of the American College of Cardiology, Vol. 42, No. 12 (2003);Gepstien, “Regenerating the Heart Using Human Embryonic Stem Cells—fromCell to Bedside”, IMAJ, Vol 8 (March 2006).

For gene therapy, see, for example: J. Kevin Donahue, “Focalmodification of electrical conduction in the heart by viral genetransfer”, Nature Medicine, Volume 6, Number 12 (December 2000); andKevin Donahue, M. D., “Targeted Modification of Atrial Electrophysiologyby Homogeneous Transmural Atrial Gene Transfer”, Circulation (Jan. 25,2005).

In patients with heart failure, electro-anatomical mapping primarilyinvolves the identification of areas with low potentials that areindicative of infarction as well as areas that exhibit reducedmechanical motion. Once diseased areas have been identified, therapy mayinvolve the targeted delivery of introduction of cells or genes capableof performing myocardial repair and regeneration. Recent advances in thearea of stem cell biology have provided scientists with potential toolsto develop novel strategies for myocardial regeneration. Such biologicaltherapies also affect the electrical properties of the tissue which canbe mapped using electro-anatomical mapping.

For both arrhythmia and heart failure treatment, following therapydelivery, electro-anatomical mapping may be used to validate therapyeffectiveness. For example, this may be accomplished during the sameprocedure to validate conduction block in areas where ablation energywas delivered or to validate the regenerated mechanical motion followingbiological implantation. Alternatively, or in addition, a scheduledfollow up may be performed several months after the procedure tovalidate long term therapy effectiveness.

Other Embodiments

The methods and systems described herein are not limited to a particularhardware or software configuration, and may find applicability in manycomputing or processing environments. The methods and systems can beimplemented in hardware, or a combination of hardware and software,and/or can be implemented from commercially available modulesapplications and devices. Where the implementation of the systems andmethods described herein is at least partly based on use ofmicroprocessors, the methods and systems can be implemented in one ormore computer programs, where a computer program can be understood toinclude one or more processor executable instructions. The computerprogram(s) can execute on one or more programmable processors, and canbe stored on one or more storage medium readable by the processor(including volatile and non-volatile memory and/or storage elements),one or more input devices, and/or one or more output devices. Theprocessor thus can access one or more input devices to obtain inputdata, and can access one or more output devices to communicate outputdata. The input and/or output devices can include one or more of thefollowing: Random Access Memory (RAM), Redundant Array of IndependentDisks (RAID), floppy drive, CD, DVD, magnetic disk, internal hard drive,external hard drive, memory stick, or other storage device capable ofbeing accessed by a processor as provided herein, where suchaforementioned examples are not exhaustive, and are for illustration andnot limitation.

The computer program(s) can be implemented using one or more high levelprocedural or object-oriented programming languages to communicate witha computer system; however, the program(s) can be implemented inassembly or machine language, if desired. The language can be compiledor interpreted. The device(s) or computer systems that integrate withthe processor(s) can include, for example, a personal computer(s),workstation (e.g., Sun, HP), personal digital assistant (PDA), handhelddevice such as cellular telephone, laptop, handheld, or another devicecapable of being integrated with a processor(s) that can operate asprovided herein. Accordingly, the devices provided herein are notexhaustive and are provided for illustration and not limitation.

References to “a microprocessor” and “a processor”, or “themicroprocessor” and “the processor,” can be understood to include one ormore microprocessors that can communicate in a stand-alone and/or adistributed environment(s), and can thus be configured to communicatevia wired or wireless communications with other processors, where suchone or more processor can be configured to operate on one or moreprocessor-controlled devices that can be similar or different devices.Furthermore, references to memory, unless otherwise specified, caninclude one or more processor-readable and accessible memory elementsand/or components that can be internal to the processor-controlleddevice, external to the processor-controlled device, and can be accessedvia a wired or wireless network using a variety of communicationsprotocols, and unless otherwise specified, can be arranged to include acombination of external and internal memory devices, where such memorycan be contiguous and/or partitioned based on the application.Accordingly, references to a database can be understood to include oneor more memory associations, where such references can includecommercially available database products (e.g., SQL, Informix, Oracle)and also proprietary databases, and may also include other structuresfor associating memory such as links, queues, graphs, trees, with suchstructures provided for illustration and not limitation.

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Accordingly, other embodiments are within the scope of the followingclaims.

We claim:
 1. A method comprising: processing information related tocharacteristics of catheter shape; measuring first signals beforetreatment at catheter electrodes at each of multiple, differentpositions of a catheter in a heart cavity in response to electricalactivity in the heart cavity; determining first physiologicalinformation at multiple locations of an endocardium surface based atleast in part on the first signals and catheter shape informationprovided by the processing of the information related to thecharacteristics of the catheter shape; measuring second signals at thecatheter electrodes after the treatment; determining secondphysiological information based at least in part on the second signalsand the catheter shape information; and displaying a difference mapincluding information about changes, from the first physiologicalinformation to the second physiological information, that occurred inresponse to the treatment.
 2. The method of claim 1, wherein determiningfirst physiological information comprises accessing stored cathetershape information.
 3. The method of claim 1, wherein the catheter isconfigured to assume multiple different shapes.
 4. The method of claim1, wherein processing information related to the characteristics of thecatheter shape comprises processing information related to the relativearrangement of the catheter electrodes on the catheter.
 5. The method ofclaim 1, wherein processing information related to the characteristicsof the catheter shape comprises determining locations of the catheterelectrodes based on a location of the catheter.
 6. The method of claim5, wherein determining first physiological information comprisesdetermining the first physiological information based at least in parton the locations of the catheter electrodes.
 7. The method claim 1,further comprising: determining a position and orientation of thecatheter within the heart cavity; wherein processing information relatedto characteristics of catheter shape comprises determining positions ofthe catheter electrodes based on information about the distribution ofthe catheter electrodes on the catheter and the position and orientationof the catheter within the heart cavity.
 8. The method of claim 1,wherein processing information related to characteristics of cathetershape comprises partially computing one or more transformationfunctions.
 9. The method of claim 1, comprising processing informationrelated to characteristics of the endocardium surface includingpartially computing one or more transformation functions for convertingthe signals measured at the catheter electrodes to estimates of thefirst physiological information at the endocardium surface.
 10. A systemcomprising: a catheter comprising: multiple, spatially distributedelectrodes, the multiple electrodes configured to: measure first signalsin response to electrical activity in a heart cavity prior to treatment;and measure second signals in response to electrical activity in theheart cavity after the treatment; a processing unit configured to:process information related to characteristics of the catheter shape;determine first physiological information at multiple locations of anendocardium surface based at least in part on the first signals andcatheter shape information provided by the processing of the informationrelated to the characteristics of the catheter shape; and determinesecond physiological information at multiple locations of theendocardium surface based at least in part on the second signals and thecatheter shape information; and a display device configured to display adifference map comprising information about the difference between thefirst physiological information and the second physiologicalinformation.
 11. The system of claim 10, wherein the catheter isconfigured to assume multiple different shapes.
 12. The system of claim10, wherein the processing unit is configured to process informationrelated to the arrangement of the multiple, spatially distributedelectrodes relative to the catheter.
 13. The system of claim 10, whereinthe processing unit is further configured to partially compute one ormore transformation functions.
 14. A method comprising: processinginformation related to characteristics of catheter shape; measuringsignals at catheter electrodes at each of multiple, different catheterpositions in a heart cavity in response to electrical activity in theheart cavity; determining a frequency representation of electricalactivity at multiple locations of an endocardium surface during a heartbeat cycle, wherein the frequency representation is computed based onelectrical potential values at the catheter electrodes; and displayinginformation related to at least a portion of the frequencyrepresentation.
 15. The method of claim 14, wherein the informationrelated to the characteristics of the catheter shape is processed priorto inserting the catheter into the heart cavity.
 16. The method of claim14, wherein determining a frequency representation of electricalactivity comprises accessing stored catheter shape information providedby the processing of the information related to the characteristics ofthe catheter shape.
 17. The method of claim 14, wherein the catheterelectrodes include multiple, spatially distributed electrodes andprocessing information related to characteristics of catheter shapecomprises processing information related to a relative arrangement ofthe multiple, spatially distributed electrodes.
 18. The method of claim14, wherein processing information related to characteristics ofcatheter shape comprises determining locations of the catheterelectrodes based on a location of the catheter.
 19. The method of claim14, wherein processing information related to characteristics of thecatheter shape comprises processing information related to distributionof the catheter electrodes on the catheter.
 20. The method of claim 14,comprising: determining a position and orientation of the catheterwithin the heart cavity; wherein processing information related tocharacteristics of catheter shape comprises determining positions of thecatheter electrodes based on information about distribution of thecatheter electrodes on the catheter and the position and orientation ofthe catheter within the heart cavity.